استفاده از یادگیری ماشین برای بهبود تخمین بارش توسط داده‌های ERA5 در ایستگاه‌های هواشناسی استان آذربایجان غربی

Amjad, M., Yilmaz, M. T., Yucel, I., & Yilmaz, K. K. (2020). Performance evaluation of satellite-and model-based precipitation products over varying climate and complex topography. Journal of Hydrology, 584, 124707. https://doi.org/10.1016/j.jhydrol.2020.124707

Bagheri Khanghahi M., Hazar Jaribi A., Kamali Mohammad I., Zamani F. Forecasting Rainfall in Different Climatic Regions of Iran Using the LARS WG7 Climate Model. Water Resources and Climate Change. (2025); 1(1), 28-39. https://doi.org/10.22091/wrcc.2025.11744.1008

Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I., Weedon, G. P.,... & Wood, E. F. (2020). Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Satellite precipitation measurement: Volume 2, 625-653. https://doi.org/10.1007/978-3-030-35798-6_9

Chen, F., Gao, Y. (2018). Evaluation of precipitation trends from high-resolution satellite precipitation products over Mainland China. Climate Dynamics, 51, 3311-3331. https://doi.org/10.1007 / s00382-018-4080-z

Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A., & Maher, N. (2016). More extreme precipitation in the world’s dry and wet regions. Nature Climate Change, 6(5), 508-513. https://doi.org/10.1038/nclimate2941

Espinosa, L. A., Portela, M. M., & Gharbia, S. (2024). Assessing changes in exceptional rainfall in Portugal using ERA5-land reanalysis data (1981/1982–2022/2023). Water, 16(5), 628. https://doi.org/10.3390/w16050628

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J.,... & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly journal of the royal meteorological society, 146(730), 1999-2049. https://doi.org/10.1002/qj.3803

Houénafa, S. E., Johnson, O., Ronoh, E. K., & Moore, S. E. (2025). Hybridization of Stochastic Hydrological Models and Machine Learning Methods for Improving Rainfall-Runoff Modelling. Results in Engineering, 104079. https://doi.org/10.1016/j.rineng.2025.104079

Huang, S., Wang, S., Chen, J., Wang, C., Zhang, X., Wu, J., Chen, N. (2024). Urbanization-induced spatial and temporal patterns of local drought revealed by high-resolution fused remotely sensed datasets. Remote Sensing of Environment, 313, 114378. https://doi.org/10.1016/j.rse.2024.114378

Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Xie, P. (2014). NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version 4. NASA technical report.

Iacono, M.J.; Delamere, J.S.; Mlawer, E.J.; Shephard, M.W.; Clough, S.A.; Collins, W.D  (2008).. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. 2008, 113, D13103. https://doi.org/10.1029/2008JD009944

Jiang, S. H., Wei, L. Y., Ren, L. L., Zhang, L. Q., Wang, M. H., & Cui, H. (2023). Evaluation of IMERG, TMPA, ERA5, and CPC precipitation products over mainland China: Spatiotemporal patterns and extremes. Water Science and Engineering, 16(1), 45-56. https://doi.org/10.1016/j.wse.2022.05.001

Kalhori M, Tadayon M, Kahrizi E, Ghiasvand M. Analysis and monitoring of water resources and drought using a combination of GRACE, MODIS, and Landsat 8 satellite images (Case study: Hamedan City). Water Resources and Climate Change. (2025); 1(1), 62-74. https://doi.org/10.22091/wrcc.2025.11390.1007

Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., & Kirschbaum, D. B. (2017). So, how much of the Earth’s surface is covered by rain gauges? Bulletin of the American Meteorological Society, 98(1), 69-78. https://doi.org/10.1175/BAMS-D-14-00283.1

Komasi M, Dalvand R. Evaluation of nonparametric decision tree models for predicting scour depth of bridges. Water Resources and Climate Change. (2025); 1(1), 40-50. https://doi.org/10.22091/wrcc.2025.11363.1005

Kumar, A., Ramsankaran, R. A. A. J., Brocca, L., & Munoz-Arriola, F. (2019). A machine learning approach for improving near-real-time satellite-based rainfall estimates by integrating soil moisture. Remote Sensing, 11(19), 2221. https://doi.org/10.3390/rs11192221

Mianabadi, A., Omidvar, J., & Pourreza-Bilandi, M. (2024). Development of intensity–duration–frequency curves at the basin scale using the ERA5 reanalysis product. Journal of Drought and Climate Change Research, 2(4), 121–140 [in Persian]. https://doi.org/10.22077/jdcr.2025.8636.1098

Modaresi, F., Araghinejad, S., & Ebrahimi, K. (2018). A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and K-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions. Water resources management, 32, 243-258. https://doi.org/10.1007/s11269-017-1807-2

Nouhani, E., Babaali, H. R., & Dehghani, R. (2024). Estimation of suspended sediments in the coastal areas of the Caspian Sea using machine learning techniques. Journal of Drought and Climate Change Research. Advance online publication. [in Persian]. https://doi.org/10.22077/jdcr.2025.8983.1121

Saha, A., & Pal, S. C. (2024). Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends. Journal of Hydrology, 632, 130907. https://doi.org/10.1016/j.jhydrol.2024.130907

Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., Thépaut, J. N. (2024). The ERA5 global reanalysis from 1940 to 2022. Quarterly Journal of the Royal Meteorological Society, 150(764), 4014-4048. https://doi.org/10.1002/qj.4803

Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., Hsu, K. L. (2018). A review of global precipitation data sets: Data sources, estimation, and intercomparisons.Reviews of Geophysics,56(1), 79-107. https://doi.org/10.1002/2017RG000574

Tang, W., Qin, J., Yang, K., Zhu, F., & Zhou, X. (2021). Does ERA5 outperform satellite products in estimating atmospheric downward longwave radiation at the surface?. Atmospheric Research, 252, 105453. https://doi.org/10.1016/j.atmosres.2021.105453

Wang, Q., Xia, J., She, D., Zhang, X., Liu, J., & Zhang, Y. (2021). Assessment of the four latest long-term satellite-based precipitation products in capturing the extreme precipitation and streamflow across a humid region of southern China. Atmospheric Research, 257, 105554. https://doi.org/10.1016/j.atmosres.2021.105554

Yang, L., Shi, Z., Liu, R., & Xing, M. (2024). Evaluating the performance of global precipitation products for precipitation and extreme precipitation in arid and semiarid China. International Journal of Applied Earth Observation and Geoinformation, 130, 103888. https://doi.org/10.1016/j.jag.2024.103888

Yousefi-Kebria, A., & Nadi, M. (2023). Evaluation of the accuracy of GPM satellite precipitation estimates: A case study in Mazandaran Province. Journal of Drought and Climate Change Research, 1(3), 1–14. [in Persian]. https://doi.org/10.22077/jdcr.2023.6232.1022

Yu, C., Hu, D., Liu, M., Wang, S., & Di, Y. (2020). Spatio-temporal accuracy evaluation of three high-resolution satellite precipitation products in the China area. Atmospheric Research, 241, 104952. https://doi.org/10.1016/j.atmosres.2020.104952

Yuan, Y., & Liao, B. (2025). Evaluation of multi-source precipitation products for monitoring drought across China. Frontiers in Environmental Science, 13, 1524937. https://doi.org/10.3389/fenvs.2025.1524937

Zhou, Z., Guo, B., Xing, W., Zhou, J., Xu, F., & Xu, Y. (2020). Comprehensive evaluation of the latest GPM era IMERG and GSMaP precipitation products over mainland China. Atmospheric Research, 246, 105132. https://doi.org/10.1016/j.atmosres.2020.105132