بازسازی داده‌های گمشده اقلیمی با استفاده از ترکیب روش بازسازی چندگانه با معادلات زنجیره‌ای (MICE) و مدل‌های تقویتی یادگیری ماشین در حوضه آبریز دریاچه ارومیه

Afrifa-Yamoah, E., Mueller, U. A., Taylor, S. M., & Fisher, A. J. (2020). Missing data imputation of high-resolution temporal climate time series data. Meteorological Applications, 27(1), e1873. https://doi.org/https://doi.org/10.1002/met.1873

Alejo-Sanchez, L. E., Márquez-Grajales, A., Salas-Martínez, F., Franco-Arcega, A., López-Morales, V., Acevedo-Sandoval, O. A., González-Ramírez, C. A., & Villegas-Vega, R. (2025). Missing data imputation of climate time series: A review. MethodsX, 15, 103455. https://doi.org/10.1016/j.mex.2025.103455

Azur, M. J., Stuart, E. A., Frangakis, C., & Leaf, P. J. (2011). Multiple imputation by chained equations: what is it and how does it work? International Journal of Methods in Psychiatric Research, 20(1), 40-49. https://doi.org/https://doi.org/10.1002/mpr.329

Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA. https://doi.org/10.1145/2939672.2939785

Costa, T., Falcão, B., Mohamed, M. A., Annuk, A., & Marinho, M. (2024). Employing machine learning for advanced gap imputation in solar power generation databases. Sci Rep, 14(1), 23801. https://doi.org/10.1038/s41598-02-74342-2

Davari, S., Eslamian, S., Jamali, M., & Safavi, H. R. (2025). Application of Machine Learning Algorithms for Groundwater Level Prediction in the Najafabad Plain. Sci Rep.

Farzandi, M., Sanaeinejad, H., Rezaei-Pazhan, H., & Sarmad, M. (2022). Improving estimation of missing data in historical monthly precipitation by evolutionary methods in the semi-arid area. Environment, Development and Sustainability, 24(6), 8313-8332. https://doi.org/10.1007/s10668-021-01784-4

Fazel Najafabadi, E., & Shayannejad, M. (2025). Evaluation the efficiency of machine learning boosting methods for estimating the water quality index of the Zayandeh Rood River. Iranian Journal of Soil and Water Research, 56(5), 1355-1378. https://doi.org/10.22059/ijswr.2025.392173.-669906

Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189-1232. http://www.jstor.org/stable/2699986

Golkhatmi, N. S. N., & Farzandi, M. (2024). Enhancing Rainfall Data Consistency and Completeness: A Spatiotemporal Quality Control Approach and Missing Data Reconstruction Using MICE on Large Precipitation Datasets. Water Resources Management, 38(3), 815-833. https://doi.org/10.1007/s11269-023-03567-0

Gupta Hoshin, V., Sorooshian, S., & Yapo Patrice, O. (1999). Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration. Journal of Hydrologic Engineering, 4(2), 135-143. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2

Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1), 80-91. https://doi.org/https://doi.org/10.1016/j.jhydrol.20

Hasanpour Kashani, M., & Dinpashoh, Y. (2012). Evaluation of efficiency of different estimation methods for missing climatological data. Stochastic Environmental Research and Risk Assessment, 26(1), 59-71. https://doi.org/10.1007/s00477-011-053

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. https://doi.org/https://doi.org/10.1002/qj.3803

Hosseinpour, S., Sharafati, A., & Abghari, H. (2025). Downscaling of two selected GCM data using a hybrid deep learning method of Wavelet-CNN-LSTM in Iran. Theoretical and Applied Climatology, 156(9): 459. DOI:10.1007/s00704-025-05685-8

Jääskeläinen, E., Manninen, T., Hakkarainen, J., & Tamminen, J. (2022). Filling gaps of black-sky surface albedo of the Arctic sea ice using gradient boosting and brightness temperature data. International Journal of Applied Earth Observation and Geoinformation, 107, 102701. https://doi.org/https://doi.org/10.1016/j.jag.2022.1

Davari, S., Elamian, S., Jamali, M., & Safavi, H. R. (2025). Application of machine learning algorithms for groundwater level prediction in the Najafabad plain. Scientific Reports, 14(3), 743-752. https://doi.org/10.1038/s41598-025-32376-1

Jamali Jezeh, M., Shayannejad, M., & Hejazi, S. M. (2020). Evaluation the Performance of Filters Made of BC, PET and PP Textiles in Removing Oil Contaminants from Water [Research]. Journal of Water and Soil Science, 24(4), 295-312. https://doi.org/10.47176.jwss.24.4.42931

Jamali, M., Gohari, A., & Akhavan Saraf, G. (2024). Spatiotemporal evaluation of temperature and precipitation extremes indices over Iran under the influence of climate change. Water and Irrigation Management, 14(3), 743-752. https://doi.org/10.22059/jwim.2024.374814.1156

Jamali, M., Eslamian, S., Shayannejad, M., & Gohari, A. (2026). Observed warming–driven aridification and climate-type transitions across Iran. Journal of Arid Environments, 19(2), -. https://doi.org/10.1016/j.jaridenv.2026.105606

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.

Khosravi, G., Nafarzadegan, A. R., Nohegar, A., Fathizadeh, H., & Malekian, A. (2015). A modified distance-weighted approach for filling annual precipitation gaps: application to different climates of Iran. Theoretical and Applied Climatology, 119(1), 33-42. https://doi.org/10.1007/s00704-014-1091-5

Knoben, W. J. M., Freer, J. E., & Woods, R. A. (2019). Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrol. Earth Syst. Sci., 23(10), 4323-4331. https://doi.org/10.5194/hess-23-4323-2019

Legates, D. R., & McCabe Jr, G. J. (1999). Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241. https://doi.org/https://doi.org/10.1029/1998WR900018

Little, R., & Rubin, D. (1987). Multiple imputation for nonresponse in surveys. Wiley, 10, 9780470316696.

Matinzadeh, M. m., Fattahi, R., Shayanzadeh, M., & Abdollahi, K. (2013). Estimation and Reconstruction of Annual Maximum 24-H Rainfall Data Using Combination of Genetic Algorithm and Artificial Neural Networks Models (Case Study: Chaharmahal va Bakhtiyari Province). ijwmse, 7(22), 53. http://jwmsei.ir/article-1-245-fa.html

900. Moriasi, D., G. Arnold, J., W. Van Liew, M., L. Bingner, R., D. Harmel, R., & L. Veith, T. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885-900. https://doi.org/https://doi.org/10.13031/2013.23153

Plein, M., Feigel, G., Zeeman, M., Dormann, C. F., & Christen, A. (2025). Using Gradient Boosting for gap-filling to analyze temperature and humidity patterns in an urban weather station network in Freiburg, Germany. Urban Climate, 62, 102496. https://doi.org/https://doi.org/10.1016/j.uclim.2025.102496

Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147-177. https://doi.org/10.1037/1082-989X.7.2.147

Van Buuren, S. (2000). Multivariate imputation by chained equations: MICE V1. 0 user's manual. Leiden: TNO.

Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of statistical software, 45, 1-67.

Willmott, C. J. (1981). ON THE VALIDATION OF MODELS. Physical Geography, 2(2), 184-194. https://doi.org/10.1080/02723646.1981.10642213