参考文献核查报告 — SmartLib API 联网验证

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有DOI文献

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#	状态	原始参考文献 (APA)	修正后文献 (APA，与原始格式一致)	主要差异	验证
1	API验证通过	Afan, H. A., Wan Mohtar, W. H. M., Aksoy, M., Ahmed, A. N., Khaleel, F., Khan, M. M. H., & El-Shafie, A. (2025). A multi-functional genetic algorithm-neural network model for predicting suspended sediment loads. Water Resources Management, 39(5), 2033–2048. https://doi.org/10.1007/s11269-024-04054-w	Afan, H. A., Wan Mohtar, W. H. M., Aksoy, M., Ahmed, A. N., Khaleel, F., Khan, M. M. H., Kamel, A. H., Sherif, M., & El-Shafie, A. (2025). A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads. Water Resources Management. https://doi.org/10.1007/s11269-024-04054-w	信息一致，无差异	验证
2	API验证通过	AlDahoul, N., Essam, Y., Kumar, P., Ahmed, A. N., Sherif, M., Sefelnasr, A., & Elshafie, A. (2021). Suspended sediment load prediction using long short-term memory neural network. Scientific Reports, 11(1), 7826. https://doi.org/10.1038/s41598-021-87415-4	AlDahoul, N., Essam, Y., Kumar, P., Ahmed, A. N., Sherif, M., Sefelnasr, A., & Elshafie, A. (2021). Suspended sediment load prediction using long short-term memory neural network. Scientific Reports. https://doi.org/10.1038/s41598-021-87415-4	信息一致，无差异	验证
3	API验证通过	Allawi, M. F., Sulaiman, S. O., Sayl, K. N., Sherif, M., & El-Shafie, A. (2023). Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study. Heliyon, 9(8), e18506.	Mohammed Falah Allawi, Sadeq Oleiwi Sulaiman, Khamis Naba Sayl, Mohsen Sherif, & Ahmed El-Shafie (2023). Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study. Heliyon.	信息一致，无差异	验证
4	API验证通过	Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324	Breiman, L. (2001). Random forests. Machine Learning. https://doi.org/10.1023/A:1010933404324	信息一致，无差异	验证
5	API验证通过	Carter, J. H. (2000). The immune system as a model for pattern recognition and classification. Journal of the American Medical Informatics Association, 7(1), 28–41. https://doi.org/10.1136/jamia.2000.0070028	Carter, J. (2000). The immune system as a model for pattern recognition and classification. Journal of the American Medical Informatics Association. https://doi.org/10.1136/jamia.2000.0070028	信息一致，无差异	验证
6	API验证通过	Chen, X. Y., & Chau, K. W. (2016). A hybrid double feedforward neural network for suspended sediment load estimation. Water Resources Management, 30(7), 2179–2194. https://doi.org/10.1007/s11269-016-1281-2	Chen, X. Y., & Chau, K. W. (2016). A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation. Water Resources Management. https://doi.org/10.1007/s11269-016-1281-2	信息一致，无差异	验证
7	API验证通过	Coello, C. A. C., & Cortés, N. C. (2004). Hybridizing a genetic algorithm with an artificial immune system for global optimization. Engineering Optimization, 36(5), 607–634. https://doi.org/10.1080/03052150410001704845	Coello, C., & Cortés, N. (2004). Hybridizing a genetic algorithm with an artificial immune system for global optimization. Engineering Optimization. https://doi.org/10.1080/03052150410001704845	信息一致，无差异	验证
8	API验证通过	Covert, I., Lundberg, S. M., & Lee, S.-I. (2020). Understanding global feature contributions with additive importance measures. Advances in Neural Information Processing Systems, 33, 17212–17223.	Covert, I. C., Lundberg, S., & Lee, S. (2020). Understanding Global Feature Contributions With Additive Importance Measures. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020.	期刊 Advances in Neural Information Processing Systems, 33, 17212–17223. → ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020	验证
9	API验证通过	Doroudi, S., & Sharafati, A. (2024). A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load. Journal of Hydrology, 634, 131090. https://doi.org/10.1016/j.jhydrol.2024.131090	Siyamak Doroudi, & Ahmad Sharafati (2024). A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2024.131090	期刊 Journal of Hydrology, 634, 131090. https://doi.org/10.1016/j.jhydrol.2024.131090 → Journal of Hydrology	验证
10	API验证通过	Dorrell, R., Amy, L., Peakall, J., & McCaffrey, W. (2018). Particle size distribution controls the threshold between net sediment erosion and deposition in suspended load dominated flows. Geophysical Research Letters, 45(3), 1443–1452. https://doi.org/10.1002/2017GL076489	Dorrell, R. M., Amy, L. A., Peakall, J., & McCaffrey, W. D. (2018). Particle Size Distribution Controls the Threshold Between Net Sediment Erosion and Deposition in Suspended Load Dominated Flows. Geophysical Research Letters. https://doi.org/10.1002/2017GL076489	信息一致，无差异	验证
11	API验证通过	Gelete, G., Nourani, V., Gökçekuş, H., & Gichamo, T. (2024). Multi-step ahead suspended sediment load modeling using machine learning– multi-model approach. Earth Science Informatics, 17(1), 633–654. https://doi.org/10.1007/s12145-023-01192-4	Gelete, G., Nourani, V., Gokcekus, H., & Gichamo, T. (2024). Multi-step ahead suspended sediment load modeling using machine learning- multi-model approach. Earth Science Informatics. https://doi.org/10.1007/s12145-023-01192-4	信息一致，无差异	验证
12	未收录	Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37–45.	—	SmartLib 全球库未收录此文献类型（书籍章节 / Scholarpedia / 未被索引期刊）	不匹配
13	未收录	Grossberg, S. (2013). Recurrent neural networks. Scholarpedia, 8(2), 1888. https://doi.org/10.4249/scholarpedia.1888	—	SmartLib 全球库未收录此文献类型（书籍章节 / Scholarpedia / 未被索引期刊）	不匹配
14	API验证通过	Gul, N., Khan, A. U., Ullah, B., Khan, B. N., Almalki, H. M., Banga, A. S., & Kumar, K. (2025). Optimizing LSTM for sediment load prediction in the Swat River Basin, Pakistan: Evaluation of optimizers and activation functions. Physics and Chemistry of the Earth, Parts A/B/C, 140, 104019. https://doi.org/10.1016/j.pce.2025.104019	Nauman Gul, Afed Ullah Khan, Basir Ullah, Bakht Niaz Khan, Hamed M. Almalki, Abdulbasid S. Banga, & Kailash Kumar (2025). Optimizing LSTM for sediment load prediction in the Swat river basin, Pakistan: Evaluation of optimizers and activation functions. Physics and Chemistry of the Earth, Parts A/B/C. https://doi.org/10.1016/j.pce.2025.104019	期刊 Physics and Chemistry of the Earth, Parts A/B/C, 140, 104019. https://doi.org/10 → Physics and Chemistry of the Earth, Parts A/B/C	验证
15	API验证通过	Hanoon, M. S., Abdullatif, B. A. A., Ahmed, A. N., Razzaq, A., Birima, A. H., & El-Shafie, A. (2022). A comparison of various machine learning approaches performance for suspended sediment load of river systems: A case study in Malaysia. Earth Science Informatics, 15(1), 91–104.	Hanoon, M. S., Abdullatif, A. A. B., Ahmed, A. N., Razzaq, A., Birima, A. H., & El-Shafie, A. (2022). A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia. Earth Science Informatics.	题名 A comparison of various machine learning approaches performance for suspended sediment load of river systems: A case study in Malaysia → A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia	验证
16	API验证通过	Hazarika, B. B., & Gupta, D. (2023). Mode decomposition based large margin distribution machines for sediment load prediction. Expert Systems with Applications, 232, 120844. https://doi.org/10.1016/j.eswa.2023.120844	Barenya Bikash Hazarika, & Deepak Gupta (2023). Mode decomposition based large margin distribution machines for sediment load prediction. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2023.120844	期刊 Expert Systems with Applications, 232, 120844. https://doi.org/10.1016/j.eswa.20 → Expert Systems with Applications	验证
17	API验证通过	Hazarika, B. B., Gupta, D., & Berlin, M. (2020). Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction. Environmental Earth Sciences, 79(10), 234. https://doi.org/10.1007/s12665-020-08949-w	Hazarika, B. B., Gupta, D., & Berlin, M. (2020). Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction. Environmental Earth Sciences. https://doi.org/10.1007/s12665-020-08949-w	信息一致，无差异	验证
18	未收录	Heddam, S., Naghibi, A., Khosravi, K., & Singh, S. K. (2024). Suspended sediment load prediction and tree-based algorithms. In A. M. Melesse, O. Rahmati, K. Khosravi, & G. P. Petropoulos (Eds.), Remote sensing of soil and landsurface processes (pp. 257–269). Elsevier.	—	SmartLib 全球库未收录此文献类型（书籍章节 / Scholarpedia / 未被索引期刊）	—
19	API验证通过	Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735	Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation. https://doi.org/10.1162/neco.1997.9.8.1735	信息一致，无差异	验证
20	未收录	Kedam, N., Tiwari, D. K., Kumar, V., Khedher, K. M., & Salem, M. A. (2024). River streamflow prediction through advanced machine learning models for enhanced accuracy. Results in Engineering, 22, 102215.	—	SmartLib 全球库未收录此文献类型（书籍章节 / Scholarpedia / 未被索引期刊）	—
21	API验证通过	Khan, M. Y. A. (2025). Regional ANN model for estimating missing daily suspended sediment load in complex, heterogeneous catchments. Journal of Geochemical Exploration, 269, 107643. https://doi.org/10.1016/j.gexplo.2024.107643	Mohd Yawar Ali Khan (2025). Regional ANN model for estimating missing daily suspended sediment load in complex, heterogeneous catchments. Journal of Geochemical Exploration. https://doi.org/10.1016/j.gexplo.2024.107643	期刊 Journal of Geochemical Exploration, 269, 107643. https://doi.org/10.1016/j.gexpl → Journal of Geochemical Exploration	验证
22	API验证通过	Kundu, S., Swarnkar, S., & Agarwal, A. (2025). Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport. Environmental Monitoring and Assessment, 197(5), 603. https://doi.org/10.1007/s10661-025-14039-w	Kundu, S., Swarnkar, S., & Agarwal, A. (2025). Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-025-14039-w	信息一致，无差异	验证
23	API验证通过	Latif, S. D., Chong, K., Ahmed, A. N., Huang, Y., Sherif, M., & El-Shafie, A. (2023). Sediment load prediction in Johor river: Deep learning versus machine learning models. Applied Water Science, 13(3), 79. https://doi.org/10.1007/s13201-023-01874-w	Latif, S. D., Chong, K. L., Ahmed, A. N., Huang, Y. F., Sherif, M., & El-Shafie, A. (2023). Sediment load prediction in Johor river: deep learning versus machine learning models. Applied Water Science. https://doi.org/10.1007/s13201-023-01874-w	信息一致，无差异	验证
24	API验证通过	Li, Z., Xu, X., & Wang, K. (2023). Scale-specific variation in daily suspended sediment load in karst catchments. Catena, 221, 106745. https://doi.org/10.1016/j.catena.2022.106745	Zhenwei Li, Xianli Xu, & Kelin Wang (2023). Scale-specific variation in daily suspended sediment load in karst catchments. CATENA. https://doi.org/10.1016/j.catena.2022.106745	期刊 Catena, 221, 106745. https://doi.org/10.1016/j.catena.2022.106745 → CATENA	验证
25	API验证通过	Liu, H., Guo, C., Xiao, L., Liu, P., Du, J., & Yi, Y. (2025). Estimating over thousands of reservoirs sedimentation and effects on sediment flux through a newly proposed sediment transport model. Engineering Applications of Computational Fluid Mechanics, 19(1), 2521825. https://doi.org/10.1080/19942060.2025.2521825	Liu, H., Guo, C., Xiao, L., Liu, P., Du, J., & Yi, Y. (2025). Estimating over thousands of reservoirs sedimentation and effects on sediment flux through a newly proposed sediment transport model. Engineering Applications of Computational Fluid Mechanics. https://doi.org/10.1080/19942060.2025.2521825	信息一致，无差异	验证
26	API验证通过	Madsen, A., Reddy, S., & Chandar, S. (2023). Post-hoc interpretability for neural nlp: A survey. ACM Computing Surveys, 55(8), 1–42. https://doi.org/10.1145/3546577	Madsen, A., Reddy, S., & Chandar, S. (2023). Post-hoc Interpretability for Neural NLP: A Survey. ACM Computing Surveys. https://doi.org/10.1145/3546577	信息一致，无差异	验证
27	API验证通过	Meshram, S. G., Singh, V. P., Kisi, O., Karimi, V., & Meshram, C. (2020). Application of artificial neural networks, support vector machine and multiple model-ANN to sediment information: Application of newly developed data mining models. Hydrological Sciences Journal, 65(4), 624–637. https://doi.org/10.1080/02626667.2019.1703186	Meshram, S. G., Singh, V. P., Kisi, O., Karimi, V., & Meshram, C. (2020). Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction. Water Resources Management. https://doi.org/10.1080/02626667.2019.1703186	题名 Application of artificial neural networks, support vector machine and multiple model-ANN to sediment information: Application of newly developed data mining models → Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction 期刊 Hydrological Sciences Journal → Water Resources Management	验证
28	API验证通过	Moazenzadeh, R., Katipoğlu, O. M., Shateri, A., Nasiri, H., & Abdallah, M. (2024). Designing an explainable bio-inspired model for suspended sediment load estimation: Extreme gradient boosting coupled with marine predators algorithm. Engineering Applications of Computational Fluid Mechanics, 18(1), 2391449. https://doi.org/10.1080/19942060.2024.2391449	Moazenzadeh, R., Katipoglu, O. M., Shateri, A., Nasiri, H., & Abdallah, M. (2024). Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm. Engineering Applications of Computational Fluid Mechanics. https://doi.org/10.1080/19942060.2024.2391449	信息一致，无差异	验证
29	API验证通过	Mohammadi-Raigani, Z., Gholami, H., Mohamadifar, A., Samani, A. N., & Pradhan, B. (2024). Using an interpretable deep learning model for the prediction of riverine suspended sediment load. Environmental Science and Pollution Research, 31(22), 32480–32493.	Mohammadi-Raigani, Z., Gholami, H., Mohamadifar, A., Samani, A. N., & Pradhan, B. (2024). Using an interpretable deep learning model for the prediction of riverine suspended sediment load. Environmental Science and Pollution Research.	信息一致，无差异	验证
30	API验证通过	Moradinejad, A. (2024). Suspended load modeling of river using soft computing techniques. Water Resources Management, 38(6), 1965–1986.	Moradinejad, A. (2024). Suspended Load Modeling of River Using Soft Computing Techniques. Water Resources Management.	信息一致，无差异	验证
31	API验证通过	Nourani, V., Alizadeh, F., & Roushangar, K. (2016). Evaluation of a two-stage SVM and spatial statistics methods for modeling monthly river suspended sediment load. Water Resources Management, 30(1), 393–407. https://doi.org/10.1007/s11269-015-1168-7	Nourani, V., Alizadeh, F., & Roushangar, K. (2016). Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load. Water Resources Management. https://doi.org/10.1007/s11269-015-1168-7	信息一致，无差异	验证
32	API验证通过	Rajaee, T. (2011). Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Science of the Total Environment, 409(15), 2917–2928. https://doi.org/10.1016/j.scitotenv.2010.11.028	Taher Rajaee (2011). Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2010.11.028	信息一致，无差异	验证
33	API验证通过	Rashidi, S., Vafakhah, M., Lafdani, E. K., & Javadi, M. R. (2016). Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arabian Journal of Geosciences, 9(11), 1–15. https://doi.org/10.1007/s12517-016-2601-9	Rashidi, S., Vafakhah, M., Lafdani, E. K., & Javadi, M. R. (2016). Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-016-2601-9	信息一致，无差异	验证
34	API验证通过	Sahoo, B. B., Sankalp, S., & Kisi, O. (2023). A novel smoothing-based deep learning time-series approach for daily suspended sediment load prediction. Water Resources Management, 37(11), 4271–4292. https://doi.org/10.1007/s11269-023-03552-7	Sahoo, B. B., Sankalp, S., & Kisi, O. (2023). A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction. Water Resources Management. https://doi.org/10.1007/s11269-023-03552-7	信息一致，无差异	验证
35	API验证通过	Saleh, A., & Zulkifley, M. A. (2025). Prediction of suspended sediment load in sungai semenyih using extreme learning machines and metaheuristic optimization approach. Journal of Environmental Management, 380, 124987. https://doi.org/10.1016/j.jenvman.2025.124987	Azlan Saleh, & Mohd Asyraf Zulkifley (2025). Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2025.124987	期刊 Journal of Environmental Management, 380, 124987. https://doi.org/10.1016/j.jenv → Journal of Environmental Management	验证
36	API验证通过	Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. (2017). Recent advances in recurrent neural networks. arXiv preprint arXiv:1801.01078.	Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. (2017). Recent advances in recurrent neural networks. arXiv.	期刊 arXiv preprint arXiv:1801.01078. → arXiv	验证
37	API验证通过	Salih, S. Q., Sharafati, A., Khosravi, K., Faris, H., Kisi, O., Tao, H., Ali, M., & Yaseen, Z. M. (2020). River suspended sediment load prediction based on river discharge pattern. Water, 12(10), 2820.	Salih, S. Q., Sharafati, A., Khosravi, K., Faris, H., Kisi, O., Tao, H., Ali, M., & Yaseen, Z. M. (2020). River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrological Sciences Journal.	题名 River suspended sediment load prediction based on river discharge pattern → River suspended sediment load prediction based on river discharge information: application of newly developed data mining models 期刊 Water → Hydrological Sciences Journal	验证
38	API验证通过	Samantaray, S., & Sahoo, A. (2024). Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation. Groundwater for Sustainable Development, 26, 101178. https://doi.org/10.1016/j.gsd.2024.101178	Sandeep Samantaray, & Abinash Sahoo (2024). Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation. Groundwater for Sustainable Development. https://doi.org/10.1016/j.gsd.2024.101178	期刊 Groundwater for Sustainable Development, 26, 101178. https://doi.org/10.1016/j.g → Groundwater for Sustainable Development	验证
39	API验证通过	Sedighkia, M., Jahanshahloo, M., & Datta, B. (2024). Hybrid neuro fuzzy inference systems for simulating catchment sediment yield. International Journal of Sediment Research, 39(3), 305–316.	Mahdi Sedighkia, Manizheh Jahanshahloo, & Bithin Datta (2024). Hybrid neuro fuzzy inference systems for simulating catchment sediment yield. International Journal of Sediment Research.	信息一致，无差异	验证
40	API验证通过	Shakya, D., Deshpande, V., Kumar, B., & Agarwal, M. (2023). Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models. Artificial Intelligence Review, 56(9), 10067–10098. https://doi.org/10.1007/s10462-023-10422-6	Shakya, D., Deshpande, V., Kumar, B., & Agarwal, M. (2023). Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models. Artificial Intelligence Review. https://doi.org/10.1007/s10462-023-10422-6	信息一致，无差异	验证
41	API验证通过	Shapley, L. S. (1953). Stochastic games. Proceedings of the National Academy of Sciences, 39(10), 1095–1100. https://doi.org/10.1073/pnas.39.10.1095	SHAPLEY, L. (1953). STOCHASTIC GAMES. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.39.10.1095	信息一致，无差异	验证
42	API验证通过	Sharafati, A., Haji Seyed Asadollah, S. B., Motta, D., & Yaseen, Z. M. (2020). Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrological Sciences Journal, 65(12), 2022–2042. https://doi.org/10.1080/02626667.2020.1786571	Sharafati, A., Asadollah, S. B. H. S., Motta, D., & Yaseen, Z. M. (2020). Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2020.1786571	信息一致，无差异	验证
43	API验证通过	Shojaeezadeh, S. A., Al-Wardy, M., & Nikoo, M. R. (2024). Suspended sediment load modeling using hydro-climate variables and machine learning. Journal of Hydrology, 633, 130948.	Shahab Aldin Shojaeezadeh, Malik Al-Wardy, & Mohammad Reza Nikoo (2024). Suspended sediment load modeling using Hydro-Climate variables and Machine learning. Journal of Hydrology.	期刊 Journal of Hydrology, 633, 130948. → Journal of Hydrology	验证
44	API验证通过	Tulla, P. S., Kumar, P., Vishwakarma, D. K., Kumar, R., Kuriqi, A., Kushwaha, N. L., Rajput, J., Srivastava, A., Pham, Q. B., & Panda, K. C. (2024). Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: A case study of Bino Watershed, Uttarakhand. Theoretical and Applied Climatology, 155(5), 4023–4047.	Tulla, P. S., Kumar, P., Vishwakarma, D. K., Kumar, R., Kuriqi, A., Kushwaha, N. L., Rajput, J., Srivastava, A., Pham, Q. B., Panda, K. C., & Kisi, O. (2024). Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand. Theoretical and Applied Climatology.	信息一致，无差异	验证
45	API验证通过	Wang, B., Wei, W., Yin, Z., & Xu, L. (2024). Using machine learning to analyze the changes in extreme precipitation in southern China. Atmospheric Research, 302, 107307.	Bojun Wang, Wei Wei, Zejiang Yin, & Lianlian Xu (2024). Using machine learning to analyze the changes in extreme precipitation in southern China. Atmospheric Research.	期刊 Atmospheric Research, 302, 107307. → Atmospheric Research	验证
46	API验证通过	Wei, P., & Yu, W. (2023). Improved quantum artificial bee colony algorithm-optimized artificial intelligence models for suspended sediment load predicting. IEEE Access.	Wei, P., & Yu, W. (2025). Improved Quantum Artificial Bee Colony Algorithm-Optimized Artificial Intelligence Models for Suspended Sediment Load Predicting. IEEE Access.	期刊 IEEE Access. → IEEE Access 年份 2023 → 2025	验证
47	API验证通过	Zuo, Z., Shuai, B., Wang, G., Liu, X., Wang, X., Wang, B., & Chen, Y. (2015). Convolutional recurrent neural networks: Learning spatial dependencies for image representation. Proceedings of the IEEE conference on computer vision and pattern recognition workshops.	Zuo, Z., Shuai, B., Wang, G., Liu, X., Wang, X., Wang, B., & Chen, Y. (2015). Convolutional Recurrent Neural Networks: Learning Spatial Dependencies for Image Representation. 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW).	期刊 Proceedings of the IEEE conference on computer vision and pattern recognition wo → 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW)	验证

修正后参考文献（APA 格式，与原文格式一致）

注意：以下编号文献在 SmartLib 全球库中未检索到匹配记录（书籍章节/Scholarpedia/未被索引），保留原始引用，请手动核实：12, 13, 18, 20

[1] Afan, H. A., Wan Mohtar, W. H. M., Aksoy, M., Ahmed, A. N., Khaleel, F., Khan, M. M. H., Kamel, A. H., Sherif, M., & El-Shafie, A. (2025). A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads. Water Resources Management. https://doi.org/10.1007/s11269-024-04054-w [2] AlDahoul, N., Essam, Y., Kumar, P., Ahmed, A. N., Sherif, M., Sefelnasr, A., & Elshafie, A. (2021). Suspended sediment load prediction using long short-term memory neural network. Scientific Reports. https://doi.org/10.1038/s41598-021-87415-4 [3] Mohammed Falah Allawi, Sadeq Oleiwi Sulaiman, Khamis Naba Sayl, Mohsen Sherif, & Ahmed El-Shafie (2023). Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study. Heliyon. [4] Breiman, L. (2001). Random forests. Machine Learning. https://doi.org/10.1023/A:1010933404324 [5] Carter, J. (2000). The immune system as a model for pattern recognition and classification. Journal of the American Medical Informatics Association. https://doi.org/10.1136/jamia.2000.0070028 [6] Chen, X. Y., & Chau, K. W. (2016). A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation. Water Resources Management. https://doi.org/10.1007/s11269-016-1281-2 [7] Coello, C., & Cortés, N. (2004). Hybridizing a genetic algorithm with an artificial immune system for global optimization. Engineering Optimization. https://doi.org/10.1080/03052150410001704845 [8] Covert, I. C., Lundberg, S., & Lee, S. (2020). Understanding Global Feature Contributions With Additive Importance Measures. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020. [9] Siyamak Doroudi, & Ahmad Sharafati (2024). A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2024.131090 [10] Dorrell, R. M., Amy, L. A., Peakall, J., & McCaffrey, W. D. (2018). Particle Size Distribution Controls the Threshold Between Net Sediment Erosion and Deposition in Suspended Load Dominated Flows. Geophysical Research Letters. https://doi.org/10.1002/2017GL076489 [11] Gelete, G., Nourani, V., Gokcekus, H., & Gichamo, T. (2024). Multi-step ahead suspended sediment load modeling using machine learning- multi-model approach. Earth Science Informatics. https://doi.org/10.1007/s12145-023-01192-4 [12] Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37–45. [13] Grossberg, S. (2013). Recurrent neural networks. Scholarpedia, 8(2), 1888. https://doi.org/10.4249/scholarpedia.1888 [14] Nauman Gul, Afed Ullah Khan, Basir Ullah, Bakht Niaz Khan, Hamed M. Almalki, Abdulbasid S. Banga, & Kailash Kumar (2025). Optimizing LSTM for sediment load prediction in the Swat river basin, Pakistan: Evaluation of optimizers and activation functions. Physics and Chemistry of the Earth, Parts A/B/C. https://doi.org/10.1016/j.pce.2025.104019 [15] Hanoon, M. S., Abdullatif, A. A. B., Ahmed, A. N., Razzaq, A., Birima, A. H., & El-Shafie, A. (2022). A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia. Earth Science Informatics. [16] Barenya Bikash Hazarika, & Deepak Gupta (2023). Mode decomposition based large margin distribution machines for sediment load prediction. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2023.120844 [17] Hazarika, B. B., Gupta, D., & Berlin, M. (2020). Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction. Environmental Earth Sciences. https://doi.org/10.1007/s12665-020-08949-w [18] Heddam, S., Naghibi, A., Khosravi, K., & Singh, S. K. (2024). Suspended sediment load prediction and tree-based algorithms. In A. M. Melesse, O. Rahmati, K. Khosravi, & G. P. Petropoulos (Eds.), Remote sensing of soil and landsurface processes (pp. 257–269). Elsevier. [19] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation. https://doi.org/10.1162/neco.1997.9.8.1735 [20] Kedam, N., Tiwari, D. K., Kumar, V., Khedher, K. M., & Salem, M. A. (2024). River streamflow prediction through advanced machine learning models for enhanced accuracy. Results in Engineering, 22, 102215. [21] Mohd Yawar Ali Khan (2025). Regional ANN model for estimating missing daily suspended sediment load in complex, heterogeneous catchments. Journal of Geochemical Exploration. https://doi.org/10.1016/j.gexplo.2024.107643 [22] Kundu, S., Swarnkar, S., & Agarwal, A. (2025). Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-025-14039-w [23] Latif, S. D., Chong, K. L., Ahmed, A. N., Huang, Y. F., Sherif, M., & El-Shafie, A. (2023). Sediment load prediction in Johor river: deep learning versus machine learning models. Applied Water Science. https://doi.org/10.1007/s13201-023-01874-w [24] Zhenwei Li, Xianli Xu, & Kelin Wang (2023). Scale-specific variation in daily suspended sediment load in karst catchments. CATENA. https://doi.org/10.1016/j.catena.2022.106745 [25] Liu, H., Guo, C., Xiao, L., Liu, P., Du, J., & Yi, Y. (2025). Estimating over thousands of reservoirs sedimentation and effects on sediment flux through a newly proposed sediment transport model. Engineering Applications of Computational Fluid Mechanics. https://doi.org/10.1080/19942060.2025.2521825 [26] Madsen, A., Reddy, S., & Chandar, S. (2023). Post-hoc Interpretability for Neural NLP: A Survey. ACM Computing Surveys. https://doi.org/10.1145/3546577 [27] Meshram, S. G., Singh, V. P., Kisi, O., Karimi, V., & Meshram, C. (2020). Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction. Water Resources Management. https://doi.org/10.1080/02626667.2019.1703186 [28] Moazenzadeh, R., Katipoglu, O. M., Shateri, A., Nasiri, H., & Abdallah, M. (2024). Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm. Engineering Applications of Computational Fluid Mechanics. https://doi.org/10.1080/19942060.2024.2391449 [29] Mohammadi-Raigani, Z., Gholami, H., Mohamadifar, A., Samani, A. N., & Pradhan, B. (2024). Using an interpretable deep learning model for the prediction of riverine suspended sediment load. Environmental Science and Pollution Research. [30] Moradinejad, A. (2024). Suspended Load Modeling of River Using Soft Computing Techniques. Water Resources Management. [31] Nourani, V., Alizadeh, F., & Roushangar, K. (2016). Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load. Water Resources Management. https://doi.org/10.1007/s11269-015-1168-7 [32] Taher Rajaee (2011). Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2010.11.028 [33] Rashidi, S., Vafakhah, M., Lafdani, E. K., & Javadi, M. R. (2016). Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-016-2601-9 [34] Sahoo, B. B., Sankalp, S., & Kisi, O. (2023). A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction. Water Resources Management. https://doi.org/10.1007/s11269-023-03552-7 [35] Azlan Saleh, & Mohd Asyraf Zulkifley (2025). Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2025.124987 [36] Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. (2017). Recent advances in recurrent neural networks. arXiv. [37] Salih, S. Q., Sharafati, A., Khosravi, K., Faris, H., Kisi, O., Tao, H., Ali, M., & Yaseen, Z. M. (2020). River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrological Sciences Journal. [38] Sandeep Samantaray, & Abinash Sahoo (2024). Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation. Groundwater for Sustainable Development. https://doi.org/10.1016/j.gsd.2024.101178 [39] Mahdi Sedighkia, Manizheh Jahanshahloo, & Bithin Datta (2024). Hybrid neuro fuzzy inference systems for simulating catchment sediment yield. International Journal of Sediment Research. [40] Shakya, D., Deshpande, V., Kumar, B., & Agarwal, M. (2023). Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models. Artificial Intelligence Review. https://doi.org/10.1007/s10462-023-10422-6 [41] SHAPLEY, L. (1953). STOCHASTIC GAMES. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.39.10.1095 [42] Sharafati, A., Asadollah, S. B. H. S., Motta, D., & Yaseen, Z. M. (2020). Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2020.1786571 [43] Shahab Aldin Shojaeezadeh, Malik Al-Wardy, & Mohammad Reza Nikoo (2024). Suspended sediment load modeling using Hydro-Climate variables and Machine learning. Journal of Hydrology. [44] Tulla, P. S., Kumar, P., Vishwakarma, D. K., Kumar, R., Kuriqi, A., Kushwaha, N. L., Rajput, J., Srivastava, A., Pham, Q. B., Panda, K. C., & Kisi, O. (2024). Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand. Theoretical and Applied Climatology. [45] Bojun Wang, Wei Wei, Zejiang Yin, & Lianlian Xu (2024). Using machine learning to analyze the changes in extreme precipitation in southern China. Atmospheric Research. [46] Wei, P., & Yu, W. (2025). Improved Quantum Artificial Bee Colony Algorithm-Optimized Artificial Intelligence Models for Suspended Sediment Load Predicting. IEEE Access. [47] Zuo, Z., Shuai, B., Wang, G., Liu, X., Wang, X., Wang, B., & Chen, Y. (2015). Convolutional Recurrent Neural Networks: Learning Spatial Dependencies for Image Representation. 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW).

多格式参考文献

适用场景：国内期刊投稿、学位论文、中文出版物

[1] Afan, Haitham Abdulmohsin, Wan Mohtar, Wan Hanna Melini, Aksoy, Muammer, Ahmed, Ali Najah, Khaleel, Faidhalrahman, Khan, Md Munir Hayet, Kamel, Ammar Hatem, Sherif, Mohsen, El-Shafie, Ahmed. A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads[J]. Water Resources Management, 2025. https://doi.org/10.1007/s11269-024-04054-w [2] AlDahoul, Nouar, Essam, Yusuf, Kumar, Pavitra, Ahmed, Ali Najah, Sherif, Mohsen, Sefelnasr, Ahmed, Elshafie, Ahmed. Suspended sediment load prediction using long short-term memory neural network[J]. Scientific Reports, 2021. https://doi.org/10.1038/s41598-021-87415-4 [3] Mohammed Falah Allawi, Sadeq Oleiwi Sulaiman, Khamis Naba Sayl, Mohsen Sherif, Ahmed El-Shafie. Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study[J]. Heliyon, 2023. [4] Breiman, L. Random forests[J]. Machine Learning, 2001. https://doi.org/10.1023/A:1010933404324 [5] Carter, JH. The immune system as a model for pattern recognition and classification[J]. Journal of the American Medical Informatics Association, 2000. https://doi.org/10.1136/jamia.2000.0070028 [6] Chen, Xiao Yun, Chau, Kwok Wing. A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation[J]. Water Resources Management, 2016. https://doi.org/10.1007/s11269-016-1281-2 [7] Coello, CAC, Cortés, NC. Hybridizing a genetic algorithm with an artificial immune system for global optimization[J]. Engineering Optimization, 2004. https://doi.org/10.1080/03052150410001704845 [8] Covert, Ian C., Lundberg, Scott, Lee, Su-In. Understanding Global Feature Contributions With Additive Importance Measures[J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020. [9] Siyamak Doroudi, Ahmad Sharafati. A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load[J]. Journal of Hydrology, 2024. https://doi.org/10.1016/j.jhydrol.2024.131090 [10] Dorrell, R. M., Amy, L. A., Peakall, J., McCaffrey, W. D.. Particle Size Distribution Controls the Threshold Between Net Sediment Erosion and Deposition in Suspended Load Dominated Flows[J]. Geophysical Research Letters, 2018. https://doi.org/10.1002/2017GL076489 [11] Gelete, Gebre, Nourani, Vahid, Gokcekus, Huseyin, Gichamo, Tagesse. Multi-step ahead suspended sediment load modeling using machine learning- multi-model approach[J]. Earth Science Informatics, 2024. https://doi.org/10.1007/s12145-023-01192-4 [12] Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37–45. [13] Grossberg, S. (2013). Recurrent neural networks. Scholarpedia, 8(2), 1888. https://doi.org/10.4249/scholarpedia.1888 [14] Nauman Gul, Afed Ullah Khan, Basir Ullah, Bakht Niaz Khan, Hamed M. Almalki, Abdulbasid S. Banga, Kailash Kumar. Optimizing LSTM for sediment load prediction in the Swat river basin, Pakistan: Evaluation of optimizers and activation functions[J]. Physics and Chemistry of the Earth, Parts A/B/C, 2025. https://doi.org/10.1016/j.pce.2025.104019 [15] Hanoon, Marwah Sattar, Abdullatif, Alharazi Abdulhadi B., Ahmed, Ali Najah, Razzaq, Arif, Birima, Ahmed H., El-Shafie, Ahmed. A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia[J]. Earth Science Informatics, 2022. [16] Barenya Bikash Hazarika, Deepak Gupta. Mode decomposition based large margin distribution machines for sediment load prediction[J]. Expert Systems with Applications, 2023. https://doi.org/10.1016/j.eswa.2023.120844 [17] Hazarika, Barenya Bikash, Gupta, Deepak, Berlin, Mohanadhas. Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction[J]. Environmental Earth Sciences, 2020. https://doi.org/10.1007/s12665-020-08949-w [18] Heddam, S., Naghibi, A., Khosravi, K., & Singh, S. K. (2024). Suspended sediment load prediction and tree-based algorithms. In A. M. Melesse, O. Rahmati, K. Khosravi, & G. P. Petropoulos (Eds.), Remote sensing of soil and landsurface processes (pp. 257–269). Elsevier. [19] Hochreiter, S, Schmidhuber, J. Long short-term memory[J]. Neural Computation, 1997. https://doi.org/10.1162/neco.1997.9.8.1735 [20] Kedam, N., Tiwari, D. K., Kumar, V., Khedher, K. M., & Salem, M. A. (2024). River streamflow prediction through advanced machine learning models for enhanced accuracy. Results in Engineering, 22, 102215. [21] Mohd Yawar Ali Khan. Regional ANN model for estimating missing daily suspended sediment load in complex, heterogeneous catchments[J]. Journal of Geochemical Exploration, 2025. https://doi.org/10.1016/j.gexplo.2024.107643 [22] Kundu, Soumya, Swarnkar, Somil, Agarwal, Akshay. Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport[J]. Environmental Monitoring and Assessment, 2025. https://doi.org/10.1007/s10661-025-14039-w [23] Latif, Sarmad Dashti, Chong, K. L., Ahmed, Ali Najah, Huang, Y. F., Sherif, Mohsen, El-Shafie, Ahmed. Sediment load prediction in Johor river: deep learning versus machine learning models[J]. Applied Water Science, 2023. https://doi.org/10.1007/s13201-023-01874-w [24] Zhenwei Li, Xianli Xu, Kelin Wang. Scale-specific variation in daily suspended sediment load in karst catchments[J]. CATENA, 2023. https://doi.org/10.1016/j.catena.2022.106745 [25] Liu, Hongxi, Guo, Chao, Xiao, Leling, Liu, Pei, Du, Jizeng, Yi, Yujun. Estimating over thousands of reservoirs sedimentation and effects on sediment flux through a newly proposed sediment transport model[J]. Engineering Applications of Computational Fluid Mechanics, 2025. https://doi.org/10.1080/19942060.2025.2521825 [26] Madsen, Andreas, Reddy, Siva, Chandar, Sarath. Post-hoc Interpretability for Neural NLP: A Survey[J]. ACM Computing Surveys, 2023. https://doi.org/10.1145/3546577 [27] Meshram, Sarita Gajbhiye, Singh, Vijay P., Kisi, Ozgur, Karimi, Vahid, Meshram, Chandrashekhar. Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction[J]. Water Resources Management, 2020. https://doi.org/10.1080/02626667.2019.1703186 [28] Moazenzadeh, Roozbeh, Katipoglu, Okan Mert, Shateri, Ahmadreza, Nasiri, Hamid, Abdallah, Mohammed. Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm[J]. Engineering Applications of Computational Fluid Mechanics, 2024. https://doi.org/10.1080/19942060.2024.2391449 [29] Mohammadi-Raigani, Zeinab, Gholami, Hamid, Mohamadifar, Aliakbar, Samani, Aliakbar Nazari, Pradhan, Biswajeet. Using an interpretable deep learning model for the prediction of riverine suspended sediment load[J]. Environmental Science and Pollution Research, 2024. [30] Moradinejad, Amir. Suspended Load Modeling of River Using Soft Computing Techniques[J]. Water Resources Management, 2024. [31] Nourani, Vahid, Alizadeh, Farhad, Roushangar, Kiyoumars. Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load[J]. Water Resources Management, 2016. https://doi.org/10.1007/s11269-015-1168-7 [32] Taher Rajaee. Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers[J]. Science of the Total Environment, 2011. https://doi.org/10.1016/j.scitotenv.2010.11.028 [33] Rashidi, Sharareh, Vafakhah, Mehdi, Lafdani, Elham Kakaei, Javadi, Mohammad Reza. Evaluating the support vector machine for suspended sediment load forecasting based on gamma test[J]. Arabian Journal of Geosciences, 2016. https://doi.org/10.1007/s12517-016-2601-9 [34] Sahoo, Bibhuti Bhusan, Sankalp, Sovan, Kisi, Ozgur. A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction[J]. Water Resources Management, 2023. https://doi.org/10.1007/s11269-023-03552-7 [35] Azlan Saleh, Mohd Asyraf Zulkifley. Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach[J]. Journal of Environmental Management, 2025. https://doi.org/10.1016/j.jenvman.2025.124987 [36] Salehinejad, Hojjat, Sankar, Sharan, Barfett, Joseph, Colak, Errol, Valaee, Shahrokh. Recent advances in recurrent neural networks[J]. arXiv, 2017. [37] Salih, Sinan Q., Sharafati, Ahmad, Khosravi, Khabat, Faris, Hossam, Kisi, Ozgur, Tao, Hai, Ali, Mumtaz, Yaseen, Zaher Mundher. River suspended sediment load prediction based on river discharge information: application of newly developed data mining models[J]. Hydrological Sciences Journal, 2020. [38] Sandeep Samantaray, Abinash Sahoo. Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation[J]. Groundwater for Sustainable Development, 2024. https://doi.org/10.1016/j.gsd.2024.101178 [39] Mahdi Sedighkia, Manizheh Jahanshahloo, Bithin Datta. Hybrid neuro fuzzy inference systems for simulating catchment sediment yield[J]. International Journal of Sediment Research, 2024. [40] Shakya, Deepti, Deshpande, Vishal, Kumar, Bimlesh, Agarwal, Mayank. Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models[J]. Artificial Intelligence Review, 2023. https://doi.org/10.1007/s10462-023-10422-6 [41] SHAPLEY, LS. STOCHASTIC GAMES[J]. Proceedings of the National Academy of Sciences, 1953. https://doi.org/10.1073/pnas.39.10.1095 [42] Sharafati, Ahmad, Asadollah, Seyed Babak Haji Seyed, Motta, Davide, Yaseen, Zaher Mundher. Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis[J]. Hydrological Sciences Journal, 2020. https://doi.org/10.1080/02626667.2020.1786571 [43] Shahab Aldin Shojaeezadeh, Malik Al-Wardy, Mohammad Reza Nikoo. Suspended sediment load modeling using Hydro-Climate variables and Machine learning[J]. Journal of Hydrology, 2024. [44] Tulla, Paramjeet Singh, Kumar, Pravendra, Vishwakarma, Dinesh Kumar, Kumar, Rohitashw, Kuriqi, Alban, Kushwaha, Nand Lal, Rajput, Jitendra, Srivastava, Aman, Pham, Quoc Bao, Panda, Kanhu Charan, Kisi, Ozgur. Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand[J]. Theoretical and Applied Climatology, 2024. [45] Bojun Wang, Wei Wei, Zejiang Yin, Lianlian Xu. Using machine learning to analyze the changes in extreme precipitation in southern China[J]. Atmospheric Research, 2024. [46] Wei, Peng, Yu, Wang. Improved Quantum Artificial Bee Colony Algorithm-Optimized Artificial Intelligence Models for Suspended Sediment Load Predicting[J]. IEEE Access, 2025. [47] Zuo, Zhen, Shuai, Bing, Wang, Gang, Liu, Xiao, Wang, Xingxing, Wang, Bing, Chen, Yushi. Convolutional Recurrent Neural Networks: Learning Spatial Dependencies for Image Representation[J]. 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2015.

适用场景：语言文学、人文艺术类英文论文

Afan, Haitham Abdulmohsin, Wan Mohtar, Wan Hanna Melini, Aksoy, Muammer, Ahmed, Ali Najah, Khaleel, Faidhalrahman, Khan, Md Munir Hayet, Kamel, Ammar Hatem, Sherif, Mohsen, and El-Shafie, Ahmed. "A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads." Water Resources Management, 2025. AlDahoul, Nouar, Essam, Yusuf, Kumar, Pavitra, Ahmed, Ali Najah, Sherif, Mohsen, Sefelnasr, Ahmed, and Elshafie, Ahmed. "Suspended sediment load prediction using long short-term memory neural network." Scientific Reports, 2021. Mohammed Falah Allawi, Sadeq Oleiwi Sulaiman, Khamis Naba Sayl, Mohsen Sherif, and Ahmed El-Shafie. "Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study." Heliyon, 2023. Breiman, L. "Random forests." Machine Learning, 2001. Carter, JH. "The immune system as a model for pattern recognition and classification." Journal of the American Medical Informatics Association, 2000. Chen, Xiao Yun, and Chau, Kwok Wing. "A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation." Water Resources Management, 2016. Coello, CAC, and Cortés, NC. "Hybridizing a genetic algorithm with an artificial immune system for global optimization." Engineering Optimization, 2004. Covert, Ian C., Lundberg, Scott, and Lee, Su-In. "Understanding Global Feature Contributions With Additive Importance Measures." ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020. Siyamak Doroudi, and Ahmad Sharafati. "A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load." Journal of Hydrology, 2024. Dorrell, R. M., Amy, L. A., Peakall, J., and McCaffrey, W. D.. "Particle Size Distribution Controls the Threshold Between Net Sediment Erosion and Deposition in Suspended Load Dominated Flows." Geophysical Research Letters, 2018. Gelete, Gebre, Nourani, Vahid, Gokcekus, Huseyin, and Gichamo, Tagesse. "Multi-step ahead suspended sediment load modeling using machine learning- multi-model approach." Earth Science Informatics, 2024. Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37–45. Grossberg, S. (2013). Recurrent neural networks. Scholarpedia, 8(2), 1888. https://doi.org/10.4249/scholarpedia.1888 Nauman Gul, Afed Ullah Khan, Basir Ullah, Bakht Niaz Khan, Hamed M. Almalki, Abdulbasid S. Banga, and Kailash Kumar. "Optimizing LSTM for sediment load prediction in the Swat river basin, Pakistan: Evaluation of optimizers and activation functions." Physics and Chemistry of the Earth, Parts A/B/C, 2025. Hanoon, Marwah Sattar, Abdullatif, Alharazi Abdulhadi B., Ahmed, Ali Najah, Razzaq, Arif, Birima, Ahmed H., and El-Shafie, Ahmed. "A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia." Earth Science Informatics, 2022. Barenya Bikash Hazarika, and Deepak Gupta. "Mode decomposition based large margin distribution machines for sediment load prediction." Expert Systems with Applications, 2023. Hazarika, Barenya Bikash, Gupta, Deepak, and Berlin, Mohanadhas. "Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction." Environmental Earth Sciences, 2020. Heddam, S., Naghibi, A., Khosravi, K., & Singh, S. K. (2024). Suspended sediment load prediction and tree-based algorithms. In A. M. Melesse, O. Rahmati, K. Khosravi, & G. P. Petropoulos (Eds.), Remote sensing of soil and landsurface processes (pp. 257–269). Elsevier. Hochreiter, S, and Schmidhuber, J. "Long short-term memory." Neural Computation, 1997. Kedam, N., Tiwari, D. K., Kumar, V., Khedher, K. M., & Salem, M. A. (2024). River streamflow prediction through advanced machine learning models for enhanced accuracy. Results in Engineering, 22, 102215. Mohd Yawar Ali Khan. "Regional ANN model for estimating missing daily suspended sediment load in complex, heterogeneous catchments." Journal of Geochemical Exploration, 2025. Kundu, Soumya, Swarnkar, Somil, and Agarwal, Akshay. "Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport." Environmental Monitoring and Assessment, 2025. Latif, Sarmad Dashti, Chong, K. L., Ahmed, Ali Najah, Huang, Y. F., Sherif, Mohsen, and El-Shafie, Ahmed. "Sediment load prediction in Johor river: deep learning versus machine learning models." Applied Water Science, 2023. Zhenwei Li, Xianli Xu, and Kelin Wang. "Scale-specific variation in daily suspended sediment load in karst catchments." CATENA, 2023. Liu, Hongxi, Guo, Chao, Xiao, Leling, Liu, Pei, Du, Jizeng, and Yi, Yujun. "Estimating over thousands of reservoirs sedimentation and effects on sediment flux through a newly proposed sediment transport model." Engineering Applications of Computational Fluid Mechanics, 2025. Madsen, Andreas, Reddy, Siva, and Chandar, Sarath. "Post-hoc Interpretability for Neural NLP: A Survey." ACM Computing Surveys, 2023. Meshram, Sarita Gajbhiye, Singh, Vijay P., Kisi, Ozgur, Karimi, Vahid, and Meshram, Chandrashekhar. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction." Water Resources Management, 2020. Moazenzadeh, Roozbeh, Katipoglu, Okan Mert, Shateri, Ahmadreza, Nasiri, Hamid, and Abdallah, Mohammed. "Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm." Engineering Applications of Computational Fluid Mechanics, 2024. Mohammadi-Raigani, Zeinab, Gholami, Hamid, Mohamadifar, Aliakbar, Samani, Aliakbar Nazari, and Pradhan, Biswajeet. "Using an interpretable deep learning model for the prediction of riverine suspended sediment load." Environmental Science and Pollution Research, 2024. Moradinejad, Amir. "Suspended Load Modeling of River Using Soft Computing Techniques." Water Resources Management, 2024. Nourani, Vahid, Alizadeh, Farhad, and Roushangar, Kiyoumars. "Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load." Water Resources Management, 2016. Taher Rajaee. "Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers." Science of the Total Environment, 2011. Rashidi, Sharareh, Vafakhah, Mehdi, Lafdani, Elham Kakaei, and Javadi, Mohammad Reza. "Evaluating the support vector machine for suspended sediment load forecasting based on gamma test." Arabian Journal of Geosciences, 2016. Sahoo, Bibhuti Bhusan, Sankalp, Sovan, and Kisi, Ozgur. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction." Water Resources Management, 2023. Azlan Saleh, and Mohd Asyraf Zulkifley. "Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach." Journal of Environmental Management, 2025. Salehinejad, Hojjat, Sankar, Sharan, Barfett, Joseph, Colak, Errol, and Valaee, Shahrokh. "Recent advances in recurrent neural networks." arXiv, 2017. Salih, Sinan Q., Sharafati, Ahmad, Khosravi, Khabat, Faris, Hossam, Kisi, Ozgur, Tao, Hai, Ali, Mumtaz, and Yaseen, Zaher Mundher. "River suspended sediment load prediction based on river discharge information: application of newly developed data mining models." Hydrological Sciences Journal, 2020. Sandeep Samantaray, and Abinash Sahoo. "Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation." Groundwater for Sustainable Development, 2024. Mahdi Sedighkia, Manizheh Jahanshahloo, and Bithin Datta. "Hybrid neuro fuzzy inference systems for simulating catchment sediment yield." International Journal of Sediment Research, 2024. Shakya, Deepti, Deshpande, Vishal, Kumar, Bimlesh, and Agarwal, Mayank. "Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models." Artificial Intelligence Review, 2023. SHAPLEY, LS. "STOCHASTIC GAMES." Proceedings of the National Academy of Sciences, 1953. Sharafati, Ahmad, Asadollah, Seyed Babak Haji Seyed, Motta, Davide, and Yaseen, Zaher Mundher. "Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis." Hydrological Sciences Journal, 2020. Shahab Aldin Shojaeezadeh, Malik Al-Wardy, and Mohammad Reza Nikoo. "Suspended sediment load modeling using Hydro-Climate variables and Machine learning." Journal of Hydrology, 2024. Tulla, Paramjeet Singh, Kumar, Pravendra, Vishwakarma, Dinesh Kumar, Kumar, Rohitashw, Kuriqi, Alban, Kushwaha, Nand Lal, Rajput, Jitendra, Srivastava, Aman, Pham, Quoc Bao, Panda, Kanhu Charan, and Kisi, Ozgur. "Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand." Theoretical and Applied Climatology, 2024. Bojun Wang, Wei Wei, Zejiang Yin, and Lianlian Xu. "Using machine learning to analyze the changes in extreme precipitation in southern China." Atmospheric Research, 2024. Wei, Peng, and Yu, Wang. "Improved Quantum Artificial Bee Colony Algorithm-Optimized Artificial Intelligence Models for Suspended Sediment Load Predicting." IEEE Access, 2025. Zuo, Zhen, Shuai, Bing, Wang, Gang, Liu, Xiao, Wang, Xingxing, Wang, Bing, and Chen, Yushi. "Convolutional Recurrent Neural Networks: Learning Spatial Dependencies for Image Representation." 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2015.

适用场景：历史学、出版业、部分人文社科期刊

Afan, Haitham Abdulmohsin, Wan Mohtar, Wan Hanna Melini, Aksoy, Muammer, Ahmed, Ali Najah, Khaleel, Faidhalrahman, Khan, Md Munir Hayet, Kamel, Ammar Hatem, Sherif, Mohsen, and El-Shafie, Ahmed. "A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads." Water Resources Management (2025). AlDahoul, Nouar, Essam, Yusuf, Kumar, Pavitra, Ahmed, Ali Najah, Sherif, Mohsen, Sefelnasr, Ahmed, and Elshafie, Ahmed. "Suspended sediment load prediction using long short-term memory neural network." Scientific Reports (2021). Mohammed Falah Allawi, Sadeq Oleiwi Sulaiman, Khamis Naba Sayl, Mohsen Sherif, and Ahmed El-Shafie. "Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study." Heliyon (2023). Breiman, L. "Random forests." Machine Learning (2001). Carter, JH. "The immune system as a model for pattern recognition and classification." Journal of the American Medical Informatics Association (2000). Chen, Xiao Yun, and Chau, Kwok Wing. "A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation." Water Resources Management (2016). Coello, CAC, and Cortés, NC. "Hybridizing a genetic algorithm with an artificial immune system for global optimization." Engineering Optimization (2004). Covert, Ian C., Lundberg, Scott, and Lee, Su-In. "Understanding Global Feature Contributions With Additive Importance Measures." ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020 (2020). Siyamak Doroudi, and Ahmad Sharafati. "A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load." Journal of Hydrology (2024). Dorrell, R. M., Amy, L. A., Peakall, J., and McCaffrey, W. D.. "Particle Size Distribution Controls the Threshold Between Net Sediment Erosion and Deposition in Suspended Load Dominated Flows." Geophysical Research Letters (2018). Gelete, Gebre, Nourani, Vahid, Gokcekus, Huseyin, and Gichamo, Tagesse. "Multi-step ahead suspended sediment load modeling using machine learning- multi-model approach." Earth Science Informatics (2024). Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37–45. Grossberg, S. (2013). Recurrent neural networks. Scholarpedia, 8(2), 1888. https://doi.org/10.4249/scholarpedia.1888 Nauman Gul, Afed Ullah Khan, Basir Ullah, Bakht Niaz Khan, Hamed M. Almalki, Abdulbasid S. Banga, and Kailash Kumar. "Optimizing LSTM for sediment load prediction in the Swat river basin, Pakistan: Evaluation of optimizers and activation functions." Physics and Chemistry of the Earth, Parts A/B/C (2025). Hanoon, Marwah Sattar, Abdullatif, Alharazi Abdulhadi B., Ahmed, Ali Najah, Razzaq, Arif, Birima, Ahmed H., and El-Shafie, Ahmed. "A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia." Earth Science Informatics (2022). Barenya Bikash Hazarika, and Deepak Gupta. "Mode decomposition based large margin distribution machines for sediment load prediction." Expert Systems with Applications (2023). Hazarika, Barenya Bikash, Gupta, Deepak, and Berlin, Mohanadhas. "Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction." Environmental Earth Sciences (2020). Heddam, S., Naghibi, A., Khosravi, K., & Singh, S. K. (2024). Suspended sediment load prediction and tree-based algorithms. In A. M. Melesse, O. Rahmati, K. Khosravi, & G. P. Petropoulos (Eds.), Remote sensing of soil and landsurface processes (pp. 257–269). Elsevier. Hochreiter, S, and Schmidhuber, J. "Long short-term memory." Neural Computation (1997). Kedam, N., Tiwari, D. K., Kumar, V., Khedher, K. M., & Salem, M. A. (2024). River streamflow prediction through advanced machine learning models for enhanced accuracy. Results in Engineering, 22, 102215. Mohd Yawar Ali Khan. "Regional ANN model for estimating missing daily suspended sediment load in complex, heterogeneous catchments." Journal of Geochemical Exploration (2025). Kundu, Soumya, Swarnkar, Somil, and Agarwal, Akshay. "Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport." Environmental Monitoring and Assessment (2025). Latif, Sarmad Dashti, Chong, K. L., Ahmed, Ali Najah, Huang, Y. F., Sherif, Mohsen, and El-Shafie, Ahmed. "Sediment load prediction in Johor river: deep learning versus machine learning models." Applied Water Science (2023). Zhenwei Li, Xianli Xu, and Kelin Wang. "Scale-specific variation in daily suspended sediment load in karst catchments." CATENA (2023). Liu, Hongxi, Guo, Chao, Xiao, Leling, Liu, Pei, Du, Jizeng, and Yi, Yujun. "Estimating over thousands of reservoirs sedimentation and effects on sediment flux through a newly proposed sediment transport model." Engineering Applications of Computational Fluid Mechanics (2025). Madsen, Andreas, Reddy, Siva, and Chandar, Sarath. "Post-hoc Interpretability for Neural NLP: A Survey." ACM Computing Surveys (2023). Meshram, Sarita Gajbhiye, Singh, Vijay P., Kisi, Ozgur, Karimi, Vahid, and Meshram, Chandrashekhar. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction." Water Resources Management (2020). Moazenzadeh, Roozbeh, Katipoglu, Okan Mert, Shateri, Ahmadreza, Nasiri, Hamid, and Abdallah, Mohammed. "Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm." Engineering Applications of Computational Fluid Mechanics (2024). Mohammadi-Raigani, Zeinab, Gholami, Hamid, Mohamadifar, Aliakbar, Samani, Aliakbar Nazari, and Pradhan, Biswajeet. "Using an interpretable deep learning model for the prediction of riverine suspended sediment load." Environmental Science and Pollution Research (2024). Moradinejad, Amir. "Suspended Load Modeling of River Using Soft Computing Techniques." Water Resources Management (2024). Nourani, Vahid, Alizadeh, Farhad, and Roushangar, Kiyoumars. "Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load." Water Resources Management (2016). Taher Rajaee. "Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers." Science of the Total Environment (2011). Rashidi, Sharareh, Vafakhah, Mehdi, Lafdani, Elham Kakaei, and Javadi, Mohammad Reza. "Evaluating the support vector machine for suspended sediment load forecasting based on gamma test." Arabian Journal of Geosciences (2016). Sahoo, Bibhuti Bhusan, Sankalp, Sovan, and Kisi, Ozgur. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction." Water Resources Management (2023). Azlan Saleh, and Mohd Asyraf Zulkifley. "Prediction of suspended sediment load in Sungai Semenyih using extreme learning machines and metaheuristic optimization approach." Journal of Environmental Management (2025). Salehinejad, Hojjat, Sankar, Sharan, Barfett, Joseph, Colak, Errol, and Valaee, Shahrokh. "Recent advances in recurrent neural networks." arXiv (2017). Salih, Sinan Q., Sharafati, Ahmad, Khosravi, Khabat, Faris, Hossam, Kisi, Ozgur, Tao, Hai, Ali, Mumtaz, and Yaseen, Zaher Mundher. "River suspended sediment load prediction based on river discharge information: application of newly developed data mining models." Hydrological Sciences Journal (2020). Sandeep Samantaray, and Abinash Sahoo. "Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation." Groundwater for Sustainable Development (2024). Mahdi Sedighkia, Manizheh Jahanshahloo, and Bithin Datta. "Hybrid neuro fuzzy inference systems for simulating catchment sediment yield." International Journal of Sediment Research (2024). Shakya, Deepti, Deshpande, Vishal, Kumar, Bimlesh, and Agarwal, Mayank. "Predicting total sediment load transport in rivers using regression techniques, extreme learning and deep learning models." Artificial Intelligence Review (2023). SHAPLEY, LS. "STOCHASTIC GAMES." Proceedings of the National Academy of Sciences (1953). Sharafati, Ahmad, Asadollah, Seyed Babak Haji Seyed, Motta, Davide, and Yaseen, Zaher Mundher. "Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis." Hydrological Sciences Journal (2020). Shahab Aldin Shojaeezadeh, Malik Al-Wardy, and Mohammad Reza Nikoo. "Suspended sediment load modeling using Hydro-Climate variables and Machine learning." Journal of Hydrology (2024). Tulla, Paramjeet Singh, Kumar, Pravendra, Vishwakarma, Dinesh Kumar, Kumar, Rohitashw, Kuriqi, Alban, Kushwaha, Nand Lal, Rajput, Jitendra, Srivastava, Aman, Pham, Quoc Bao, Panda, Kanhu Charan, and Kisi, Ozgur. "Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand." Theoretical and Applied Climatology (2024). Bojun Wang, Wei Wei, Zejiang Yin, and Lianlian Xu. "Using machine learning to analyze the changes in extreme precipitation in southern China." Atmospheric Research (2024). Wei, Peng, and Yu, Wang. "Improved Quantum Artificial Bee Colony Algorithm-Optimized Artificial Intelligence Models for Suspended Sediment Load Predicting." IEEE Access (2025). Zuo, Zhen, Shuai, Bing, Wang, Gang, Liu, Xiao, Wang, Xingxing, Wang, Bing, and Chen, Yushi. "Convolutional Recurrent Neural Networks: Learning Spatial Dependencies for Image Representation." 2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) (2015).

适用场景：LaTeX文档、Zotero/EndNote导入

@article{ref001, author = {{Afan, Haitham Abdulmohsin} and {Wan Mohtar, Wan Hanna Melini} and {Aksoy, Muammer} and {Ahmed, Ali Najah} and {Khaleel, Faidhalrahman} and {Khan, Md Munir Hayet} and {Kamel, Ammar Hatem} and {Sherif, Mohsen} and {El-Shafie, Ahmed}}, title = {A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads}, journal = {Water Resources Management}, year = {2025}, doi = {10.1007/s11269-024-04054-w} } @article{ref002, author = {{AlDahoul, Nouar} and {Essam, Yusuf} and {Kumar, Pavitra} and {Ahmed, Ali Najah} and {Sherif, Mohsen} and {Sefelnasr, Ahmed} and {Elshafie, Ahmed}}, title = {Suspended sediment load prediction using long short-term memory neural network}, journal = {Scientific Reports}, year = {2021}, doi = {10.1038/s41598-021-87415-4} } @article{ref003, author = {{Mohammed Falah Allawi} and {Sadeq Oleiwi Sulaiman} and {Khamis Naba Sayl} and {Mohsen Sherif} and {Ahmed El-Shafie}}, title = {Suspended sediment load prediction modelling based on artificial intelligence methods: The tropical region as a case study}, journal = {Heliyon}, year = {2023} } @article{ref004, author = {{Breiman, L}}, title = {Random forests}, journal = {Machine Learning}, year = {2001}, doi = {10.1023/A:1010933404324} } @article{ref005, author = {{Carter, JH}}, title = {The immune system as a model for pattern recognition and classification}, journal = {Journal of the American Medical Informatics Association}, year = {2000}, doi = {10.1136/jamia.2000.0070028} } @article{ref006, author = {{Chen, Xiao Yun} and {Chau, Kwok Wing}}, title = {A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation}, journal = {Water Resources Management}, year = {2016}, doi = {10.1007/s11269-016-1281-2} } @article{ref007, author = {{Coello, CAC} and {Cortés, NC}}, title = {Hybridizing a genetic algorithm with an artificial immune system for global optimization}, journal = {Engineering Optimization}, year = {2004}, doi = {10.1080/03052150410001704845} } @article{ref008, author = {{Covert, Ian C.} and {Lundberg, Scott} and {Lee, Su-In}}, title = {Understanding Global Feature Contributions With Additive Importance Measures}, journal = {ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020}, year = {2020} } @article{ref009, author = {{Siyamak Doroudi} and {Ahmad Sharafati}}, title = {A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load}, journal = {Journal of Hydrology}, year = {2024}, doi = {10.1016/j.jhydrol.2024.131090} } @article{ref010, author = {{Dorrell, R. M.} and {Amy, L. A.} and {Peakall, J.} and {McCaffrey, W. D.}}, title = {Particle Size Distribution Controls the Threshold Between Net Sediment Erosion and Deposition in Suspended Load Dominated Flows}, journal = {Geophysical Research Letters}, year = {2018}, doi = {10.1002/2017GL076489} } @article{ref011, author = {{Gelete, Gebre} and {Nourani, Vahid} and {Gokcekus, Huseyin} and {Gichamo, Tagesse}}, title = {Multi-step ahead suspended sediment load modeling using machine learning- multi-model approach}, journal = {Earth Science Informatics}, year = {2024}, doi = {10.1007/s12145-023-01192-4} } % [12] NOT_FOUND - Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence lab % [13] NOT_FOUND - Grossberg, S. (2013). Recurrent neural networks. Scholarpedia, 8(2), 1888. https @article{ref014, author = {{Nauman Gul} and {Afed Ullah Khan} and {Basir Ullah} and {Bakht Niaz Khan} and {Hamed M. Almalki} and {Abdulbasid S. Banga} and {Kailash Kumar}}, title = {Optimizing LSTM for sediment load prediction in the Swat river basin, Pakistan: Evaluation of optimizers and activation functions}, journal = {Physics and Chemistry of the Earth, Parts A/B/C}, year = {2025}, doi = {10.1016/j.pce.2025.104019} } @article{ref015, author = {{Hanoon, Marwah Sattar} and {Abdullatif, Alharazi Abdulhadi B.} and {Ahmed, Ali Najah} and {Razzaq, Arif} and {Birima, Ahmed H.} and {El-Shafie, Ahmed}}, title = {A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia}, journal = {Earth Science Informatics}, year = {2022} } @article{ref016, author = {{Barenya Bikash Hazarika} and {Deepak Gupta}}, title = {Mode decomposition based large margin distribution machines for sediment load prediction}, journal = {Expert Systems with Applications}, year = {2023}, doi = {10.1016/j.eswa.2023.120844} } @article{ref017, author = {{Hazarika, Barenya Bikash} and {Gupta, Deepak} and {Berlin, Mohanadhas}}, title = {Modeling suspended sediment load in a river using extreme learning machine and twin support vector regression with wavelet conjunction}, journal = {Environmental Earth Sciences}, year = {2020}, doi = {10.1007/s12665-020-08949-w} } % [18] NOT_FOUND - Heddam, S., Naghibi, A., Khosravi, K., & Singh, S. K. (2024). Suspended sediment @article{ref019, author = {{Hochreiter, S} and {Schmidhuber, J}}, title = {Long short-term memory}, journal = {Neural Computation}, year = {1997}, doi = {10.1162/neco.1997.9.8.1735} } % [20] NOT_FOUND - Kedam, N., Tiwari, D. K., Kumar, V., Khedher, K. M., & Salem, M. A. (2024). Rive @article{ref021, author = {{Mohd Yawar Ali Khan}}, title = {Regional ANN model for estimating missing daily suspended sediment load in complex, heterogeneous catchments}, journal = {Journal of Geochemical Exploration}, year = {2025}, doi = {10.1016/j.gexplo.2024.107643} } @article{ref022, author = {{Kundu, Soumya} and {Swarnkar, Somil} and {Agarwal, Akshay}}, title = {Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport}, journal = {Environmental Monitoring and Assessment}, year = {2025}, doi = {10.1007/s10661-025-14039-w} } @article{ref023, author = {{Latif, Sarmad Dashti} and {Chong, K. L.} and {Ahmed, Ali Najah} and {Huang, Y. F.} and {Sherif, Mohsen} and {El-Shafie, Ahmed}}, title = {Sediment load prediction in Johor river: deep learning versus machine learning models}, journal = {Applied Water Science}, year = {2023}, doi = {10.1007/s13201-023-01874-w} } @article{ref024, author = {{Zhenwei Li} and {Xianli Xu} and {Kelin Wang}}, title = {Scale-specific variation in daily suspended sediment load in karst catchments}, journal = {CATENA}, year = {2023}, doi = {10.1016/j.catena.2022.106745} } @article{ref025, author = {{Liu, Hongxi} and {Guo, Chao} and {Xiao, Leling} and {Liu, Pei} and {Du, Jizeng} and {Yi, Yujun}}, title = {Estimating over thousands of reservoirs sedimentation and effects on sediment flux through a newly proposed sediment transport model}, journal = {Engineering Applications of Computational Fluid Mechanics}, year = {2025}, doi = {10.1080/19942060.2025.2521825} } @article{ref026, author = {{Madsen, Andreas} and {Reddy, Siva} and {Chandar, Sarath}}, title = {Post-hoc Interpretability for Neural 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引用统计分析

年份分布（1953 - 2025）

期刊集中度（Top 10）

作者合作模式分析

独立作者：6 篇

合作论文（≥3作者）：29 篇

去重作者总数：152 人

平均作者数/篇：3.5

整体建议

高引期刊集中在 Water Resources Management、Journal of Hydrology 等水资源领域期刊，引文来源专业对口
4 篇未收录文献为书籍章节和 Scholarpedia 条目，非传统期刊文献，建议补充 DOI 或 ISBN
#27 Meshram 引用期刊名有误：原写 Hydrological Sciences Journal，应为 Water Resources Management
#37 Salih 标题和期刊均有差异，建议根据 DOI 核实原文
#46 Wei 年份标注有误：原写 2023，数据库记录为 2025
年份跨度从 1953 到 2025，覆盖经典文献到最新研究，参考文献时效性和经典性兼顾良好