ارزیابی کارایی مدل‌ LSTM در پیش‌بینی جریان روزانه ورودی به مخازن سدها

Babaei, M., Moeini, R., & Ehsanzadeh, E. (2019). Artificial neural network and support vector machine models for inflow prediction of dam reservoir (case study: Zayandehroud dam reservoir). Water Resources Management, 33, 2203-2218.

Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123-140.

Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.

Cheng, M., Fang, F., Kinouchi, T., Navon, I. M., & Pain, C. C. (2020). Long lead-time daily and monthly streamflow forecasting using machine learning methods. Journal of Hydrology, 590, 125376.

Dehghani, M., Saghafian, B., Rivaz, F., & Khodadadi, A. (2015). Monthly stream flow forecasting via dynamic spatio-temporal models. Stochastic environmental research and risk assessment, 29, 861-874.

Fathian, F., Mehdizadeh, S., Sales, A. K., & Safari, M. J. S. (2019). Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models. Journal of Hydrology, 575, 1200-1213.

Han, H., Choi, C., Jung, J., & Kim, H. S. (2021). Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow. Journal of Korea Water Resources Association, 54(3), 157-166.

Hochreiter, S., Schmidhuber, J., (1997). Long short-term memory. Neural Comput. 9, 1735–1780.

Hu, C., Wu, Q., Li, H., Jian, S., Li, N., & Lou, Z. (2018). Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water, 10(11), 1543.

Hu, R., Fang, F., Pain, C. C., & Navon, I. M. (2019). Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method. Journal of Hydrology, 575, 911-920.

Khorram, S., & Jehbez, N. (2023). A hybrid CNN-LSTM approach for monthly reservoir inflow forecasting. Water Resources Management, 37(10), 4097-4121.

Kişi, Ö. (2009). "Evolutionary fuzzy models for river suspended sediment concentration estimation." Journal of Hydrology. 372(1–4): 68-79.

Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Herrnegger, M. (2018). Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 22(11), 6005-6022.

Kumar, S., Tiwari, M. K., Chatterjee, C., & Mishra, A. (2015). Reservoir inflow forecasting using ensemble models based on neural networks, wavelet analysis, and bootstrap method. Water resources management, 29(13), 4863-4883.

Latif, S. D., Ahmed, A. N., Sathiamurthy, E., Huang, Y. F., & El-Shafie, A. (2021). Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia. Natural Hazards, 109(1), 351-369

Le, X. H., Ho, H. V., Lee, G., & Jung, S. (2019). Application of long short-term memory (LSTM) neural network for flood forecasting. Water, 11(7), 1387.

Lee, S., & Kim, J. (2021). Predicting inflow rate of the Soyang river dam using deep learning techniques. Water, 13(17), 2447.

Man, Y., Yang, Q., Shao, J., Wang, G., Bai, L., & Xue, Y. (2023). Enhanced LSTM model for daily runoff prediction in the upper Huai River Basin, China. Engineering, 24, 229-238.

Milly, P. C., Dunne, K. A., & Vecchia, A. V. (2005). Global pattern of trends in streamflow and water availability in a changing climate. Nature, 438(7066), 347-350.

Misaghi, F. (2016). Forecasting of the Alavian Dam Inflow Water Using Optimized Adaptive Neuro-Fuzzy Inference System (OANFIS). Iranian Journal of Soil and Water Research, 47(3), 439-448. )In Persian(.

Mouatadid, S., Adamowski, J. F., Tiwari, M. K., & Quilty, J. M. (2019). Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting. Agricultural Water Management, 219, 72-85.

Mousavi Nadushni, S, S., BaniHabib, M. E., & Bandari, R. (2013). prediction of daily inflow Dez dam reservoir using time series models, 11th National Seminar on Irrigation and Evaporation Reduction, Kerman, https://civilica .com/doc/157849. )In Persian(.

Mousavi, S. F., & Mahjoobi, J. (2019). Real-time streamflow prediction for optimal water resource management: Challenges and advances. Journal of Hydrology, 123(4), 567-581. https://doi.org/10.1016/j.jhydrol.2019.08.015

National Flood Report Special Committee (2019), Hydrology and Water Resources Working Group, Reservoir Management Committee. (In Persian).

Nayak, P. C., K. P. Sudheer, D. M. Rangan & K. S. Ramasastri, (2004). "A neuro-fuzzy computing technique for modeling hydrological time series." Journal of Hydrology 291(1–2): 52-66.

Ni, L., Wang, D., Singh, V. P., Wu, J., Wang, Y., Tao, Y., & Zhang, J. (2020). Streamflow and rainfall forecasting by two long short-term memory-based models. Journal of Hydrology, 583, 124296.

Noorbeh, P., Roozbahani, A., & Kardan Moghaddam, H. (2020). Annual and monthly dam inflow prediction using Bayesian networks. Water Resources Management, 34, 2933-2951.

Nourani, V. & M. Komasi (2013). "A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process." Journal of Hydrology 490: 41-55.

Nourani, V. (2017). An emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology, 544, 267-277.

Nourani, V., & Komasi, M. (2013). A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process. Journal of Hydrology, 490, 41-55.

Nourani, V., & Partoviyan, A. (2018). Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling. Stochastic environmental research and risk assessment, 32(2), 545-562.

Olah, C. (2015). Understanding LSTM Networks Browning KA, Collier CG (1989) Nowcasting of precipitation systems. Rev Geophys 27:345– 370. https://doi.org/10.1029/RG027i003p00345.

Qi, Y., Zhou, Z., Yang, L., Quan, Y., & Miao, Q. (2019). A decomposition-ensemble learning model based on LSTM neural network for daily reservoir inflow forecasting. Water Resources Management, 33, 4123-4139.

Ross, T. J. (1995). Fuzzy logic with engineering application. McGraw Hill Inc., USA,.

Samadi Borojni, H. & Ostad Ali Askari, Mansour. (2008). Real-time forecasting of daily inflow to the reservoir of Karun Dam 3 by combining the autoregressive method and rainfall forecasts. Iran Water Resources Research, 4(2), 14-22. )In Persian(.

Shahkarami, N., Sanikhani, H., & Moradi, M. (2016). Prediction of inflow to Kamal Saleh dam reservoir using soft calculations. Scientific Journal of Irrigation and Water Engineering of Iran, 6(2), 48-61. )In Persian(.

Sun, F., & Burian, S. J. (2016). Enhancing the accuracy of daily streamflow predictions using machine learning models. Journal of Hydrology, 540, 225-237.

Xiao, Z., Liang, Z., Li, B., Hou, B., Hu, Y., & Wang, J. (2019). New flood early warning and forecasting method based on similarity theory. Journal of Hydrologic Engineering, 24(8), 04019023.

Yaseen, Z. M., El-Shafie, A., Jaafar, O., Afan, H. A., & Sayl, K. N. (2015). Artificial intelligence based models for stream-flow forecasting: 2000–2015. Journal of Hydrology, 530, 829-844.

Zhang, X., Wang, H., Peng, A., Wang, W., Li, B., & Huang, X. (2020). Quantifying the uncertainties in data-driven models for reservoir inflow prediction. Water Resources Management, 34(4), 1479-1493.