Ali, S., Khorrami, B., Jehanzaib, M., Tariq, A., Ajmal, M., Arshad, A., Shafeeque, M., Dilawar, A., Basit, I., & Zhang, L. (2023). Spatial downscaling of GRACE data based on an XGBoost model for improved understanding of hydrological droughts in the Indus Basin Irrigation System (IBIS). Remote Sensing, 15(4), 873. https://doi.org/10.3390/rs15040873
Alimkulov, S., Makhmudova, L., Satenova, B., Tursunova, A., Birimbayeva, L., Talipova, E., Abdibekov, D., Smagulov, Z., & Alzhanov, O. (2025). Modelling Daily River Discharge Using Machine Learning Ensembles in the Context of Climate Change: Application to the Zhaiyk-Caspian Basin, Kazakhstan. Earth Systems and Environment, 1-28. https://doi.org/10.1007/s41748-025-00858-x
Almazroui, M., Islam, M. N., Saeed, F., Saeed, S., Ismail, M., Ehsan, M. A., Diallo, I., O’Brien, E., Ashfaq, M., & Martínez-Castro, D. (2021). Projected changes in temperature and precipitation over the United States, Central America, and the Caribbean in CMIP6 GCMs. Earth Systems and Environment, 5(1), 1-24. https://doi.org/10.1007/s41748-021-00199-5.
Bartlett, J. A., & Dedekorkut-Howes, A. (2023). Adaptation strategies for climate change impacts on water quality: a systematic review of the literature. Journal of Water and Climate Change, 14(3), 651-675. https://doi.org/DOI: 10.2166/wcc.2022.279.
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
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
Dey, R., & Mathur, R. (2023). An ensemble learning method using stacking with a base learner for comparison. International Conference on Data Analytics and Insights, https://doi.org/10.1007/978-981-99-3878-0_14
Dey, S. (2025). Short-Term River Discharge Forecasting Using an XGBoost-Based Regression Model.
Dhaliwal, D. S., & Williams, M. M. (2024). Sweet corn yield prediction using machine learning models and field-level data. Precision Agriculture, 25(1), 51-64. https://doi.org/10.1007/s11119-023-10057-1
Dong, J., Zeng, W., Wu, L., Huang, J., Gaiser, T., & Srivastava, A. K. (2023). Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China. Engineering Applications of Artificial Intelligence, 117, 105579. https://doi.org/10.1016/j.engappai.2022.105579
Fan, J., Wu, L., Zhang, F., Cai, H., Wang, X., Lu, X., & Xiang, Y. (2018). Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modelling based on sunshine duration and air temperature. Renewable and Sustainable Energy Reviews, 94, 732-747. https://doi.org/10.1016/j.rser.2018.06.029
Foruzanmehr, M., & Khozeymehnezhad, H. (2023). Investigation of spatial and temporal changes of qualitative parameters of the Qaen plain aquifer using interpolation methods. Journal of Aquifer and Qanat, 4(1), 1-15. https://doi.org/10.22077/jaaq.2023.5130.1045
Fouladi Nasrabad, M., Pourreza Bilondi, M., Amirabadizadeh, M., & Javaheri, M. (2025). Performance of the XGBoost algorithm in downscaling temperature and relative humidity in a temperate climate: a case study in Kermanshah. Water and Soil Management and Modelling, 5(Special Issue: Climate Change and Effects on Water and Soil), 215-232. https://doi.org/10.22098/mmws.2025.18179.1661
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. https://doi.org/10.1214/aos/1013203451
Ghorbani, M. A., Shamshirband, S., Haghi, D. Z., Azani, A., Bonakdari, H., & Ebtehaj, I. (2017). Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil and Tillage Research, 172, 32-38. https://doi.org/10.1016/j.still.2017.04.009
Hastie, T. (2009). The elements of statistical learning: data mining, inference, and prediction. In: Springer.
Hosen, N., Nakamura, H., & Hamzah, A. (2020). Adaptation to climate change: Does traditional ecological knowledge hold the key? Sustainability, 12(2), 676. https://doi.org/10.3390/su12020676.
Hosseini Fahraji, R. S., & Sharifzadeh, M. (2016). Qanat maintenance from key informants' viewpoint: A qualitative research in Taft County. The Journal of Community Development (Rural-Urban), 8(2), 295-312. https://doi.org/DOI: 10.22059/jrd.2016.63069
Jiang, X., Fan, C., Liu, K., Chen, T., Cao, Z., & Song, C. (2022). Centenary covariations of water salinity and storage of the largest lake of Northwest China reconstructed by machine learning. Journal of Hydrology, 612, 128095. https://doi.org/10.1016/j.jhydrol.2022.128095.
Khoshsimaie Chenar, M., Noory, H., Liaghat, A., Soltani Salehabadi, F., & Motesharezadeh, B. (2025). Evaluation of the accuracy of different machine learning algorithms in predicting greenhouse cucumber crop evapotranspiration. Water and Irrigation Management, 15(3), 563-583. https://doi.org/10.22059/jwim.2025.399994.1251
Khosravi, Y., Ouarda, T. B., & Homayouni, S. (2025). Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East. npj Climate and Atmospheric Science, 8(1), 174. https://doi.org/doi:10.1038/s41612-025-01033-9
Knoben, W. J., Freer, J. E., & Woods, R. A. (2019). Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrology and Earth System Sciences, 23(10), 4323-4331. https://doi.org/10.5194/hess-23-4323-2019
Kramer, O. (2016). Scikit-learn. In Machine learning for evolution strategies (pp. 45-53). Springer. https://doi.org/10.1007/978-3-319-33383-0_5
Lane, R. A., & Kay, A. L. (2021). Climate change impact on the magnitude and timing of hydrological extremes across Great Britain. Frontiers in Water, 3, 684982. https://doi.org/DOI: 10.3389/frwa 2021.684982.
Liu, X. (2025). Enhanced Prediction of Karst Spring Discharge Using a Hybrid LSTM-XGBoost Model Optimised with Grid Search. Machine Learning With Applications, 100740. https://doi.org/10.1016/j.mlwa.2025.100740
Mahammad, S., Islam, A., Shit, P. K., Islam, A. R. M. T., & Alam, E. (2023). Groundwater level dynamics in a subtropical fan delta region and its future prediction using machine learning tools: Sustainable groundwater restoration. Journal of Hydrology: Regional Studies, 47, 101385. https://doi.org/10.1016/j.ejrh.2023.101385
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology,
Mohtasham, M., Dehghani, A. A., Akbarpour, A., & Meftah, M. (2017). Evaluation of Artificial Neural Networks and MODFLOW Numerical Model in Forecasting Groundwater Table (Case Study: Birjand Aquifer, Southern Khorasan). Iranian Journal of Irrigation & Drainage, 11(1), 1-10. https://idj.iaid.ir/article_79421_a03adf46f077c337759e1acd7ad06fd5.pdf
Mosavi, A., Ozturk, P., & Chau, K.-w. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536
Mostafazadeh, R., & Zabihi, M. (2016). Comparison of SPI and SPEI indices to meteorological drought assessment using R programming (Case study: Kurdistan Province). Journal of the Earth and Space Physics, 42(3), 633-643. https://doi.org/10.22059/jesphys.2016.57881
Natekin, A., & Knoll, A. (2013). Gradient Boosting Machines: A Tutorial. Frontiers in neurorobotics, 7, 21. https://doi.org/10.3389/fnbot.2013.00021
Niazkar, M., Menapace, A., Brentan, B., Piraei, R., Jimenez, D., Dhawan, P., & Righetti, M. (2024). Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023). Environmental Modelling & Software, 174, 105971. https://doi.org/10.1016/j.envsoft.2024.105971
Nourani, V., Behfar, N., Dabrowska, D., & Zhang, Y. (2021). The applications of soft computing methods for seepage modelling: A review. Water, 13(23), 3384. https://doi.org/10.3390/w13233384
Nourani, V., Razzaghzadeh, Z., Baghanam, A. H., & Molajou, A. (2019). ANN-based statistical downscaling of climatic parameters using a decision tree predictor screening method. Theoretical and Applied Climatology, 137(3), 1729-1746. https://doi.org/10.1007/s00704-018-2686-z
Ogunrinde, A. T., Adeyeri, O. E., Xian, X., Yu, H., Jing, Q., & Faloye, O. T. (2024). Long-term spatiotemporal trends in precipitation, temperature, and evapotranspiration across arid Asia and Africa. Water, 16(22), 3161. https://doi.org/10.3390/w16223161.
Piraei, R., Afzali, S. H., & Niazkar, M. (2023). Assessment of XGBoost to estimate total sediment loads in rivers. Water Resources Management, 37(13), 5289-5306. https://doi.org/10.1007/s11269-023-03606-w
Rakhshandehroo, G., Akbari, H., Afshari Igder, M., & Ostadzadeh, E. (2018). Long-term groundwater-level forecasting in shallow and deep wells using wavelet neural networks trained by an improved harmony search algorithm. Journal of Hydrologic Engineering, 23(2), 04017058.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat, F. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. https://doi.org/10.1038/s41586-019-0912-1
Safira, R. A. D., Anjasmara, I. M., & Awange, J. L. (2024). Predictive Modelling of Terrestrial Water Storage Anomalies in Kalimantan Basins: Bridging the GRACE and GRACE-FO Data Gap with Extreme Gradient Boosting. GEOID, 19(3), 508-518. https://doi.org/10.12962/geoid.v19i3.2329
Samani, S., Vadiati, M., Kisi, O., Ghasemi, L., & Farajzadeh, R. (2024). Qanat discharge prediction using a comparative analysis of machine learning methods. Earth Science Informatics, 17(5), 4597-4618. https://doi.org/10.1007/s11600-022-00964-8
Samaniego, L. (2025). Permanent shifts in the global water cycle. Science, 387(6741), 1348-1350. https://doi.org/ 10.1126/science.adw5851.
Scanlon, B. R., Fakhreddine, S., Rateb, A., de Graaf, I., Famiglietti, J., Gleeson, T., Grafton, R. Q., Jobbagy, E., Kebede, S., & Kolusu, S. R. (2023). Global water resources and the role of groundwater in a resilient water future. Nature Reviews Earth & Environment, 4(2), 87-101. https://doi.org/10.1038/s43017-022-00378-6.
Shams, M. Y., Elshewey, A. M., El-kenawy, E.-S. M., Ibrahim, A., Talaat, F. M., & Tarek, Z. (2024). Water quality prediction using machine learning models based on the grid search method. Multimedia Tools and Applications, 83(12), 35307-35334. https://doi.org/10.1007/s11042-023-16737-4
Shrestha, N., & Shukla, S. (2015). Support vector machine-based modelling of evapotranspiration using hydro-climatic variables in a sub-tropical environment. Agricultural and forest meteorology, 200, 172-184. https://doi.org/10.1016/j.agrformet.2014.09.025
Stringer, L. C., Mirzabaev, A., Benjaminsen, T. A., Harris, R. M., Jafari, M., Lissner, T. K., Stevens, N., & Tirado-von Der Pahlen, C. (2021). Climate change impacts on water security in global drylands. One Earth, 4(6), 851-864. https://doi.org/10.1016/j.oneear.2021.05.010.
Sun, K., Gao, Y., He, W., Wang, L., & Sun, X. (2025). Prediction of soil-water retention curves in unsaturated soils based on stacked generalisation. Journal of Rock Mechanics and Geotechnical Engineering. https://doi.org/10.1016/j.jrmge.2025.12.016
Vapnik, V. N. (2000). The Nature of Statistical Learning Theory. Springer New York, NY. https://doi.org/10.1007/978-1-4757-3264-1
Wu, J., Ma, D., & Wang, W. (2022). Leakage identification in water distribution networks based on the XGBoost algorithm. Journal of Water Resources Planning and Management, 148(3), 04021107. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001523
Yang, D., Yang, Y., & Xia, J. (2021). Hydrological cycle and water resources in a changing world: A review. Geography and Sustainability, 2(2), 115-122. https://doi.org/10.1016/j.geosus.2021.05.003.
Zhou, S., Liu, Z., Wang, M., Gan, W., Zhao, Z., & Wu, Z. (2022). Impacts of building configurations on urban stormwater management at a block scale using XGBoost. Sustainable Cities and Society, 87, 104235. https://doi.org/10.1016/j.scs.2022.104235
Zounemat-Kermani, M., Mahdavi-Meymand, A., Alizamir, M., Adarsh, S., & Yaseen, Z. M. (2020). On the complexities of sediment load modelling using integrative machine learning: Application of the great river of Loíza in Puerto Rico. Journal of Hydrology, 585, 124759. https://doi.org/10.1016/j.jhydrol.2020.124759
