ارزیابی مدل‌های یادگیری ماشین مبتنی بر MICE در بازسازی داده‌های گمشده اقلیمی در حوضه دریاچه ارومیه

منابع

ستاری، م. ت. شیرینی، ک. و جاویدان، س. (1403). ارزیابی کارائی روش‌های کاهش بعد در بهبود دقت مدل‌سازی شاخص کیفی آب با استفاده از الگوریتم‌های یادگیری ماشین. مدل‌سازی مدیریت آب و خاک، 4(2)، 89-104. doi: 10.22098/mmws.2023.12434.1241

محتشم، جاوید. 1402. بازسازی داده‌های بارندگی روزانه دارای دوره‌های مفقودی، با استفاده از روش‌های محاسبه چندگانه با تطبیق میانگین پیش‌بینی و جنگل تصادفی. پایان نامه کارشناسی ارشد، دانشگاه صنعتی اصفهان. 278 ص.

وفائی ممقانی، آ. اسدی، ا. دربندی، ص. و ستاری، م. ت. (1405). کاربست و مقایسه روش‌های درونیابی داده‌های گمشده‌ی تراز آب زیرزمینی با تأکید بر عملکرد DeepMVI (منطقه مورد مطالعه: دشت عجبشیر). مدلسازی مدیریت آب و خاک، 6 (1)، 17-36. doi: 10.22098/mmws.2025.17457.1601

References

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, 103.455. doi:10.1016/j.mex.2025.103455

Amekudzi, L. K., Gyasi-Agyei, Y., Obuobie, E., & Addi, M. (2022). Evaluation of imputation techniques for infilling missing daily rainfall records on river basins in Ghana. Taylor & Francis.

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. doi:10.1002/mpr.329

Chivers, B. D., Wallbank, J., Cole, S. J., Sebek, O., Stanley, S., Fry, M., & Leontidis, G. (2020). Imputation of missing sub-hourly precipitation data in a large sensor network: A machine learning approach. Journal of Hydrology, 588, 125126. :doi:10.1145/2939672.2939785

Davari, S., Eslamian, S., Jamali, M., & Safavi, H. R. (2025). Application of machine learning algorithms for groundwater level prediction in the Najafabad plain. Scientific Reports. doi:10.47176/jwss.24.4.42931  

Diouf, S., Deme, A., El Hadji Deme, P. F., & Diouf, I. (2023). An evaluation of the performance of imputation methods for missing meteorological data in Burkina Faso and Senegal. Afr. J. Environ. Sci. Technol, 17, 252-274. doi:10.47176/jwss.24.4.42933

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. doi:10.1007/s10668-021-01784-4

Géron, A., (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow.  O'Reilly Media, Inc.

Ghanavati, R., Salajegheh, A., Pourghasemi, H. R., Khalighi Sigaroodi, S., & Keshtkar, H. (2025). Evaluation of machine learning techniques (SVM, GLM, FDA, RF) in preparing flood susceptibility map of a part of Khuzestan province. Water and Soil Management and Modeling, 5(1), 231-246. doi:10.47176/jwss.24.4.42905

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. doi: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. doi:10.1061/(ASCE)1084-0699

Hamzah, F. B., Mohamad Hamzah, F., Mohd Razali, S. F., & El-Shafie, A. (2022). Multiple imputations by chained equations for recovering missing daily streamflow observations: A case study of Langat River basin in Malaysia. Hydrological Sciences Journal, 67(1), 137-149.

Hassanzadeh, E., Zarghami, M. and Hassanzadeh, Y., (2012). Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resources Management, 26(1), pp.129-145.

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. doi:10.1016/j.jag.2022.102701

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), 29. (In Persian) doi:10.47176/jwss.24.4.42931

Jamali, M., & Eslamian, S. (2023). Parametric and nonparametric methods for analyzing the trend of extreme events. In Handbook of Hydroinformatics (pp. 223-237). Elsevier.

Jamali, M., & Eslamian, S. (2024). Climate Adaptation and Water–Environment–Energy Nexus. In Handbook of Climate Change Impacts on River Basin Management (pp. 225-236). CRC Press.

Jing, X., Luo, J., Wang, J., Zuo, G., & Wei, N. (2022). A multi-imputation method to deal with hydro-meteorological missing values by integrating chain equations and random forest. Water Resources Management, 36(4), 1159-1173.

Karbasi, M., 2016. Reconstruction of missing data of monthly total sunshine hours using artificial neural networks. Iranian Journal of Irrigation & Drainage, 10(5), pp.570-580.

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.

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 System. Science., doi:10.5194/hess-23-4323-2019

Kumar, G. P., & Dwarakish, G. (2025). Comparison of the multiple imputation approaches for imputing rainfall data: A humid tropical river basin case study. Water Conservation Science and Engineering, 10(2), 87.

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. doi:10.1029/1998WR900018

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

Little, R. J., & Rubin, D. B. (2019). Statistical analysis with missing data. John Wiley & Sons.

Liu, J., Jiang, L., Zhang, X., Druce, D., Kittel, C. M., Tøttrup, C., & Bauer-Gottwein, P. (2021). Impacts of water resources management on land water storage in the North China Plain: Insights from multi-mission earth observations. Journal of Hydrology, 603, 126933.

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. doi:10.13031/2013.23153

O'Sullivan, B., & Kelly, G. (2024). Infilling of high‐dimensional rainfall networks through multiple imputation by chained equations. International Journal of Climatology, 44(9), 3075-3091.

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. doi:10.1016/j.uclim.2025.102496

Sahoo, A. and Ghose, D. K. 2022. Imputation of missing precipitation data using KNN, SOM, RF, and FNN. Soft Computing. 26: 5919-5936.

Qaraghuli, K., Murshed, M., Said, M. A. M., Mokhtar, A., & Rousta, I. (2024). Univariate and multivariate imputation methods evaluation for reconstructing climate time series data: A case study of Mosul station-Iraq. Journal of Agrometeorology, 26(3), 318-323.

Tashman, L.J., (2000). Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting, 16(4), pp.437-450.

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

Wilks, D.S., 2011. Statistical methods in the atmospheric sciences (Vol. 100). Academic press.

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