پیش بینی روزانه جریان رودخانه سیمینه با استفاده از مدل های یادگیری ماشین و عمیق تقویت شده با مدل بهینه سازی عقاب

Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-Qaness, M. A. A., & Gandomi, A. H. (2021). Aquila

 

optimizer: a novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250. https://doi.org/10.1016/j.cie.2021.107250

Ahmadaali, J., Barani, G. A., Qaderi, K., & Hessari, B. (2017). Calibration and validation of model WEAP21 for Zarrineh Rud and Simineh Rud basins. Iranian Journal of Soil and Water Research, 48(4), 823–839. https://doi.org/10.22059/ijswr.2017.216989.667543

Akiner, M. E., Kartal, V., Guzeler, A. C., & Karakoyun, E. (2024). Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation. Earth Science Informatics, 17(4), 3111–3135. https://doi.org/10.1609/aaai.v32i1.12255

Bargam, B., Boudhar, A., Kinnard, C., Bouamri, H., Nifa, K., & Chehbouni, A. (2024). Evaluation of the support vector regression (SVR) and the random forest (RF) models accuracy for streamflow prediction under a data-scarce basin in Morocco. Discover Applied Sciences, 6(6), 306. https://doi.org/10.1007/s42452-024-05994-z

Bostanmaneshrad, F., Partani, S., Noori, R., Nachtnebel, H.-P., Berndtsson, R., & Adamowski, J. F. (2018). Relationship between water quality and macro-scale parameters (land use, erosion, geology, and population density) in the Siminehrood River Basin. Science of the Total Environment, 639, 1588–1600. https://doi.org/10.1016/j.scitotenv.2018.05.244

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324

Chen, L., Wang, Z., Jiang, Z., & Lin, X. (2024). Deep learning models for multi-step prediction of water levels incorporating meteorological variables and historical data. Stochastic Environmental Research and Risk Assessment, 1–23. https://doi.org/10.1007/s00477-024-02766-4

Danesh, M., Gharehbaghi, A., Mehdizadeh, S., & Danesh, A. (2025). A comparative assessment of machine learning and deep learning models for the daily river streamflow forecasting. Water Resources Management, 39(4), 1911–1930. https://doi.org/10.1007/s11269-024-04052-y

Guo, J., Liu, Y., Zou, Q., Ye, L., Zhu, S., & Zhang, H. (2023). Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM. Journal of Hydrology, 624, 129969. https://doi.org/10.1016/j.jhydrol.2023.129969

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

Huang, J., Chen, J., Huang, H., & Cai, X. (2025). Deep Learning-Based Daily Streamflow Prediction Model for the Hanjiang River Basin. Hydrology, 12(7), 168. https://doi.org/10.3390/hydrology12070168

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695.

Jimmy, S. R., Sahoo, A., Samantaray, S., & Ghose, D. K. (2020). Prophecy of runoff in a river basin using various neural networks. In Communication Software and Networks: Proceedings of INDIA 2019 (pp. 709–718). Springer. https://doi.org/10.1007/978-981-15-5397-4_72

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. https://doi.org/10.3390/w11071387

Lin, Y., Wang, D., Meng, Y., Sun, W., Qiu, J., Shangguan, W., Cai, J., Kim, Y., & Dai, Y. (2023). Bias learning improves data driven models for streamflow prediction. Journal of Hydrology: Regional Studies, 50, 101557. https://doi.org/10.1016/j.ejrh.2023.101557

Mangukiya, N. K., & Sharma, A. (2025). Deep learning‐based approach for enhancing streamflow prediction in watersheds with aggregated and intermittent observations. Water Resources Research, 61(1), e2024WR037331. https://doi.org/10.1029/2024WR037331

Ng, K. W., Huang, Y. F., Koo, C. H., Chong, K. L., El-Shafie, A., & Ahmed, A. N. (2023). A review of hybrid deep learning applications for streamflow forecasting. Journal of Hydrology, 625, 130141. https://doi.org/10.1016/j.jhydrol.2023.130141

Qiao, X., Peng, T., Sun, N., Zhang, C., Liu, Q., Zhang, Y., Wang, Y., & Nazir, M. S. (2023). Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation and multi-step runoff prediction. Expert Systems with Applications, 229, 120616. https://doi.org/10.1016/j.eswa.2023.120616

Rezaei, A., Babazadeh, H., Khosrojerdi, A., & Sarai-Tabrizi, M. (2025). Removal of sulfate pollutant from different samples of a river water using nanozeolite technology, case study: Gamasiab River, Iran. PloS One, 20(2), e0314480. https://doi.org/10.1371/journal.pone.0314480

Sasmal, B., Hussien, A. G., Das, A., & Dhal, K. G. (2023). A comprehensive survey on aquila optimizer. Archives of Computational Methods in Engineering, 30(7), 4449–4476. https://doi.org/10.1007/s11831-023-09945-6

Shi, W., Wang, M., Li, D., Li, X., & Sun, M. (2023). An improved method that incorporates the estimated runoff for peak discharge prediction on the Chinese Loess Plateau. International Soil and Water Conservation Research, 11(2), 290–300. https://doi.org/10.1016/j.iswcr.2022.09.001

Vatanchi, S. M., Etemadfard, H., Maghrebi, M. F., & Shad, R. (2023). A comparative study on forecasting of long-term daily streamflow using ANN, ANFIS, BiLSTM and CNN-GRU-LSTM. Water Resources Management, 37(12), 4769–4785. https://doi.org/10.21203/rs.3.rs-1443377/v1

Wang, Z., Xu, N., Bao, X., Wu, J., & Cui, X. (2024). Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion. Environmental Modelling & Software, 178, 106091. https://doi.org/10.1016/j.envsoft.2024.106091

Wrisberg, N., de Haes, H. A. U., & Triebswetter, U. (2002). Analytical tools for environmental design and management in a systems perspective: the combined use of analytical tools (Vol. 10). Springer Science & Business Media.

Xu, W., Chen, J., Corzo, G., Xu, C., Zhang, X. J., Xiong, L., Liu, D., & Xia, J. (2024). Coupling deep learning and physically based hydrological models for monthly streamflow predictions. Water Resources Research, 60(2), e2023WR035618. https://doi.org/10.1029/2023WR035618

Yao, Z., Wang, Z., Wang, D., Wu, J., & Chen, L. (2023). An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and runoff data as input. Journal of Hydrology, 625, 129977. https://doi.org/10.1016/j.jhydrol.2023.129977

Zhang, J., & Yan, H. (2023). A long short-term components neural network model with data augmentation for daily runoff forecasting. Journal of Hydrology, 617, 128853. https://doi.org/10.1016/j.jhydrol.2022.128853