روشی نوین در پیش‌بینی تغییرات آتی سطح آب‌‌زیرزمینی با الگوریتم‌های k-means و جنگل تصادفی با داده‌های اقلیمی CMIP6 در دشت اسلام‌آباد غرب

Adib, M. N. M., & Harun, S. (2022). Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting. Journal of Hydrologic Engineering, 27(6), 05022004.‏ https://doi.org/10.1061/(ASCE)HE.1943-5584.0002176

Akurugu, C. A. (2024). Identifying Drivers of Groundwater Level Fluctuations in the Casper Aquifer Near Cheyenne, WY, Using Random Forest Model (Master's thesis, University of Wyoming).‏

Alamsyah, N., Budiman, B., Yoga, T. P., & Alamsyah, R. Y. R. (2024). Comparison linear regression and random forest models for prediction of underground drought levels in forest  fires. Jurnal Techno Nusa Mandiri, 21(2), 81-86.‏ https://doi.org/10.33480/techno.v21i2.5237

Almasi, A., Fatemi, S. E., & Eghbalzadeh, A. (2024). The prediction of monthly rainfall in Kermanshah Synoptic Station under the social-economic scenarios of the sixth climate change report. Advanced Technologies in Water Efficiency, 4(1), 40-64. https://doi.org/10.22126/atwe.2024.10245.1097 (In Persian)

Amnuaylojaroen, T. (2023). Advancements in downscaling global climate model temperature data in southeast asia: a machine learning approach. Forecasting, 6(1), 1-17.‏

Araghi, A., Martinez, C. J., Adamowski, J., & Olesen, J.E. (2018). Spatiotemporal variations of aridity in Iran using high‐resolution gridded data. International Journal of Climatology, 38 (6), 2701-2717. https://doi.org/10.1002/joc.5454

Atabati, A. & Adab, H. (2022). Evaluation of machine learning methods in spatial downscaling of average annual land surface temperature and air temperature. Journal of Natural Environment, 75(4), 551-569. https://doi.org/.22059/jne.2022.340875.2416 (In Persian).

Bagherabadi, Rasoul. (2022). Investigation of meteorological drought Eslamabad-e Gharb using of draught indices. Water Engineering, 10 (2), 136_148 (In Persian).

Bagheri-Khanghahi, M., Jaribi, A. H., Kamali, M. I., & Zamani, F. (2025). Forecasting Rainfall in Different Climatic Regions of Iran Using the LARS WG7 Climate Model. Water Resources and Climate Change‏, 10.22091/wrcc.2025.11744.1008 (In Persian)

Bochenek, B., & Ustrnul, Z. (2022). Machine learning in weather prediction and climate analyses—applications and perspectives. Atmosphere, 13(2), 180.‏ https://doi.org/10.3390/atmos13020180

Bogireddy, S. R., & Murari, H. (2024, September). Enhancing crop yield prediction through random forest classifier: A comprehensive approach. In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1663-1668). IEEE.‏ http://dx.doi.org/10.1109/ICOSEC61587.2024.10722249

Boo, K. B. W., El-Shafie, A., Othman, F., Khan, M. M. H., Birima, A. H., & Ahmed, A. N. (2024). Groundwater level forecasting with machine learning models: A review. Water Research, 252, 121249.‏ https://doi.org/10.1016/j.watres.2024.121249

Darabi Cheghabaleki, S., Fatemi, S. E., & Hafezparast Mavadat, M. (2024). Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies. Applied Water Science, 14(6), 110.‏ https://doi.org/10.1007/s13201-024-02154-x

Das, S., Datta, P., Sharma, D., & Goswami, K. (2022). Trends in temperature, precipitation, potential evapotranspiration, and water availability across the teesta river basin under 1.5 and 2 C temperature rise scenarios of CMIP6. Atmosphere, 13(6), 941. https://doi.org/10.3390/atmos13060941.

Eghrari, Z., Delavar, M. R., Zare, M., Mousavi, M., Nazari, B., & Ghaffarian, S. (2023). Groundwater level prediction using deep recurrent neural networks and uncertainty assessment. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 493-500.‏ https://doi.org/10.5194/isprs-annals-X-1-W1-2023-493-2023

Esmaeili, S. & Mojarrad, F. (2025). Investigating the stability of the groundwater level in the Eslamabad-e Gharb plain (Kermanshah province) and evaluating the future situation with atmospheric general circulation models. Hydrogeomorphology, 11(41), 134-115. https://doi.org/10.22034/hyd.2024.62351.1747 (In Persian)

Fang, W., Ren, K., Liu, T., Shang, J., Jia, S., Jiang, X., & Zhang, J. (2024). An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting. Scientific Reports, 14(1), 29766.‏ https://doi.org/10.1038/s41598-024-81502-y

Fatemi, S. , Ghobadian, R. and Pakbin, M. (2018). Forecasting Groundwater Depth Using Time series Spectral Analysis. Water and Soil Science, 28(1), 145-158. https://water-soil.tabrizu.ac.ir/article_7600.html (In Persian)

Fatemi, S. E., & Parvini, H. (2022). The impact assessments of the ACF shape on time series forecasting by the ANFIS model. Neural Computing and Applications, 34(15), 12723-12736.‏ https://doi.org/10.1007/s00521-022-07140-5

Gaber, K. Sh., & Singla, M. K. (2025). Predictive analysis of groundwater resources using Random Forest regression. Journal of Artificial Intelligence and Metaheuristics, 9(1), 11–19. https://doi.org/10.54216/JAIM.090102

Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., & Takahashi, K. (2019). Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geoscientific model development, 12(4), 1443-1475. https://doi.org/10.5194/gmd-12-1443-2019

Gorgani, S., Bafkar, A., & Fatemi, S. E. (2017). Prediction of groundwater pollution potential using the DRASTIC index and annual time series analysis (case study: plain Mahidasht Kermanshah).‏ Journal of Health and Environment, 10 (3), 317-328. http://ijhe.tums.ac.ir/article-1-5962-en.pdf (In Persian)

Gupta, S. K., Sahoo, S., Sahoo, B. B., Srivastava, P. K., Pateriya, B., & Santosh, D. T. (2024). Prediction of groundwater level changes based on machine learning technique in highly groundwater irrigated alluvial aquifers of south-central Punjab, India. Physics and Chemistry of the Earth, 135, 103603.‏ https://doi.org/10.1016/j.pce.2024.103603

Irmanda, H. N., Ermatita, E., bin Awang, M. K., & Adrezo, M. (2024). Enhancing Weather Prediction Models through the Application of Random Forest Method and Chi-Square Feature Selection. International Journal on Informatics Visualization, 8(3-2), 1506-1514.‏ http://dx.doi.org/10.62527/joiv.8.3.2356

Islam, M. S. (2023). Groundwater: Sources, functions, and quality. In Hydrogeochemical evaluation and groundwater quality (pp. 17-36). Cham: Springer Nature Switzerland.‏ https://doi.org/10.1007/978-3-031-44304-6-2.

Jin, H., Jiang, W., Chen, M., Li, M., Bakar, K. S., & Shao, Q. (2023). Downscaling long lead-time daily rainfall ensemble forecasts through deep learning. Stochastic Environmental Research and Risk Assessment, 37(8), 3185-3203.‏ https://doi.org/10.1007/s00477-023-02444-x

Kardan Moghaddam, H., Ghordoyee Milan, S., Kayhomayoon, Z., Rahimzadeh kivi, Z., & Arya Azar, N. (2021). The prediction of aquifer groundwater level based on spatial clustering approach using machine learning. Environmental Monitoring and Assessment, 193(4), 173.‏ https://doi.org/10.1007/s10661-021-08961-y

Kariminezhad, H., Fatemi, S. E., & Hafezparast Mavadat, M. (2023). Reservoir inflow Classification of Jamishan reservoir by K-means method and its effect on stochastic dynamic programming. Advanced Technologies in Water Efficiency, 3(2), 15-32. https://doi.org/10.22126/atwe.2023.9111.1052 (In Persian)

Kayhomayoon, Z., Ghordoyee Milan, S., Arya Azar, N., & Kardan Moghaddam, H. (2021). A new approach for regional groundwater level simulation: clustering, simulation, and optimization. Natural Resources Research, 30, 4165-4185.‏ https://doi.org/10.1007/s11053-021-09913-6

Kayhomayoon, Z., Ghordoyee-Milan, S., Jaafari, A., Arya-Azar, N., Melesse, A. M., & Moghaddam, H. K. (2022). How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels? Computers and Electronics in Agriculture, 203, 107482.‏ https://doi.org/10.1016/j.compag.2022.107482

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

Liu, B., Sun, Y., & Gao, L. (2024). Enhancing Groundwater Recharge Prediction: A Feature Selection‐Based Deep Forest Model With Bayesian Optimisation. Hydrological Processes, 38(10), e15309.‏ https://doi.org/10.1002/hyp.15309

Mannik, M., Bikse, J., & Karro, E. (2024, April). Groundwater vulnerability and pollution risk assessment in the Estonian-Latvian transboundary area. In EGU General Assembly Conference Abstracts (p. 16280).‏ https://doi.org/10.5194/egusphere-egu24-16280

Mendoza Paz, S., Villazón Gómez, M. F., & Willems, P. (2024). Adapting to Climate Change with Machine Learning: The Robustness of Downscaled Precipitation in Local Impact Analysis. Water, 16(21), 3070.‏ https://doi.org/10.3390/w16213070

Miao, Y., & Xu, Y. (2024, June). Random Forest-Based Analysis of Variability in Feature Impacts. In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA) (pp. 1130-1135). IEEE.‏ https://doi.org/10.1109/ICIPCA61593.2024.10708791

Moeeni, H., Bonakdari, H., & Fatemi, S. E. (2017). Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction. Journal of hydrology, 547, 348-364.‏ https://doi.org/10.1016/j.jhydrol.2017.02.012

Mohammed, K. S., Shabanlou, S., Rajabi, A., Yosefvand, F., & Izadbakhsh, M. A. (2023). Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS. Applied Water Science, 13(2), 54.‏ https://doi.org/10.1007/s13201-022-01861-7

Norouzi, H. & Nadiri, A. (2018). Groundwater Level Prediction of Boukan Plain using Fuzzy Logic, Random Forest and Neural Network Models. Journal of Range and Watershed Managment, 71(3), 829-845. doi: https://doi.org/10.22059/jrwm.2018.68924 (In Persian)

O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M, (2016), The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016

Panahi, M., Misaqi, F. and Ghanbari, F. (2017). Determining of trend variation in quality parameters of Shabestar plain underground water. Environmental Sciences, 15(3), 19-38. https://envs.sbu.ac.ir/article_97862.html (In Persian)

Pang, B., Yue, J., Zhao, G., & Xu, Z. (2017). Statistical downscaling of temperature with the random forest model. Advances in Meteorology, 2017(1), 7265178.‏ https://doi.org/10.1155/2017/7265178

Pourmorad, S., Kabolizade, M., & Dimuccio, L. A. (2024). Artificial Intelligence Advancements for Accurate Groundwater Level Modelling: An Updated Synthesis and Review. Applied Sciences, 14(16), 7358.‏ https://doi.org/10.3390/app14167358

Poursalehi, F.  KhasheiSiuki, A. & Hashemi, S. R. (2021). Investigating the performance of random forest algorithm in predicting water table fluctuations Compared with two models of decision tree and artificial neural network (Case study: unconfined aquifer of Birjand plain). Journal of Ecohydrology, 8(4), 961-974. http://dx.doi.org/10.22059/ije.2022.327263.1526 (In Persian)

Rampal, N., Hobeichi, S., Gibson, P. B., Baño-Medina, J., Abramowitz, G., Beucler, T., González-Abad, J., Chapman, W., Harder, P., & Gutierrez, J. M. (2024). Enhancing regional climate downscaling through advances in machine learning. Artificial Intelligence for the Earth Systems, 3(2), 230066.‏ https://doi.org/10.1175/AIES-D-23-0066.1

Sakizadeh, M., Mohamed, M. M., & Klammler, H. (2019). Trend analysis and spatial prediction of groundwater levels using time series forecasting and a novel spatio-temporal method. Water Resources Management, 33, 1425-1437.‏ https://doi.org/10.1007/s11269-019-02208-9

Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69-79.‏ https://doi.org/10.58496/BJML/2024/007

Soltani, K., masoompour samakosh, J., Mojarrad, F., Hadi Pour, S. and Jalilian, A. (2023). Analysis of the Trend and Spatial Variation of Aridity in the Future Climate of Iran. Physical Geography Research, 55(2), 25-50. https://doi.org/10.22059/jphgr.2023.361339.1007777 (In Persian)

Tang, K., Zhu, H., & Ni, P. (2021). Spatial downscaling of land surface temperature over heterogeneous regions using random forest regression considering spatial features. Remote Sensing, 13(18), 3645.‏ https://doi.org/10.3390/rs13183645

Tang, T., Liu, T., & Gui, G. (2024). Forecasting precipitation and temperature evolution patterns under Climate Change using a Random Forest Approach with Seasonal Bias correction. Journal of Selected Topics in Applied Earth Observations and Remote Sensing.‏ 17, 12609-12621. https://doi.org/10.1109/JSTARS.2024.3425639

Teimoori, S., Olya, M. H., & Miller, C. J. (2023). Groundwater level monitoring network design with machine learning methods. Journal of Hydrology, 625, 130145.‏ https://doi.org/10.1016/j.jhydrol.2023.130145

Uc-Castillo, J. L., Marín-Celestino, A. E., Martínez-Cruz, D. A., Tuxpan-Vargas, J., & Ramos-Leal, J. A. (2023). A systematic review and meta-analysis of groundwater level forecasting with machine learning techniques: Current status and future directions. Environmental Modelling & Software, 168, 105788.‏ https://doi.org/10.1016/j.envsoft.2023.105788

Wang, P. (2024, December). Prediction of the Groundwater Levels Based on Random Forest Regression Algorithm. In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence, 4, 214-217. https://doi.org/10.1109/ICIBA62489.2024.10869277

Williams, S. A., Megdal, S. B., Zuniga-Teran, A. A., Quanrud, D. M., & Christopherson, G. (2024). Equity Assessment of Groundwater Vulnerability and Risk in Drinking Water Supplies in Arid Regions. Water (20734441), 16(23).‏ https://doi.org/10.3390/w16233520

Yang, R., Zhong, Y., Zhang, X., Maimaitituersun, A., & Ju, X. (2025). A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China. Remote Sensing, 17(3), 493.‏ https://doi.org/10.3390/rs17030493.