کاربرد مدل هوش مصنوعی ماشین آموزش نیرومند با حذف داده پرت (ORELM) در پیش‌بینی دبی رودخانه

Bărbulescu, A., & Zhen, L. (2024). Forecasting the river water discharge by artificial intelligence methods. Water, 16(9), 1248. https://doi.org/10.3390/w16091248

Darabi Cheghabaleki, S., Fatemi, S. E., & Hafezparast Mavadat, M. (2024). Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies. Applied Water Science, 14(6), 110. http://dx.doi.org/10.1007/s13201-024-02154-x

Defontaine, T., Ricci, S., Lapeyre, C. J., Marchandise, A., & Le Pape, E. (2024). Real-time flood forecasting with Machine Learning using scarce rainfall-runoff data. EGUsphere, 1-32. https://doi.org/10.5194/egusphere-2023-2621

Deo, R. C., Downs, N., Parisi, A. V., Adamowski, J. F., & Quilty, J. M. (2017). Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle. Environmental research, 155, 141-166. https://doi.org/10.1016/j.envres.2017.01.035

Fatemi, S. E., & Parvini, H. (2022). The impact assessments of the ACF shape on time series forecasting by the ANFIS model. Neural Computing and Applications, 34(15), 12723-12736. http://dx.doi.org/10.1007/s00521-022-07140-5

Gorgani, S., Bafkar, A., & Fatemi, S. (2017). Prediction of groundwater pollution potential using the DRASTIC index and annual time series analysis (case study: plain Mahidasht Kermanshah). IJHE, 10 (3), 317-328. http://ijhe.tums.ac.ir/article-1-5962-en.html (In Persian)

Huang, G., Huang, G.-B., Song, S., & You, K. (2015). Trends in extreme learning machines: A review. Neural Networks, 61, 32-48. http://dx.doi.org/10.1016/j.neunet.2014.10.001

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1-3), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126

Morovati, R., & Kisi, O. (2024). Utilizing hybrid machine learning techniques and gridded precipitation data for advanced discharge simulation in under-monitored river basins. Hydrology, 11(4), 48. https://doi.org/10.3390/hydrology11040048

Raja Shekar, P., & Mathew, A. (2024). AI-Based Rainfall-Runoff Modelling for Sustainable Water Management in Potteruvagu Watershed, India. In Geospatial Technology to Support Communities and Policy: Pathways to Resiliency, Springer, Springer, New York Sity, United states. http://dx.doi.org/10.1007/978-3-031-52561-2_6

Salcedo-Sanz, S., Pastor-Sánchez, A., Prieto, L., Blanco-Aguilera, A., & García-Herrera, R. (2014). Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization–Extreme learning machine approach. Energy Conversion and Management, 87, 10-18. https://doi.org/10.1016/j.enconman.2014.06.041

Wang, E., Zhang, Y., Luo, J., Chiew, F. H. S., & Wang, Q. J. (2011). Monthly and seasonal streamflow forecasts using rainfall-runoff modeling and historical weather data. Water Resources Research, 47(5). https://doi.org/https://doi.org/10.1029/2010WR009922

Zhang, X., Wang, R., Wang, W., Zheng, Q., Ma, R., Tang, R., & Wang, Y. (2025). Runoff prediction using combined machine learning models and signal decomposition. Journal of Water and Climate Change, 16(1), 230-247. https://doi.org/10.2166/wcc.2024.663