Applications of Machine Learning methods make a paradigm shift in the domain of water resources engineering. This study not only presents the story of emerging eXtreme Gradient Boosting (XGBoost) but also encompasses a thorough review XGBoost utilization to problems in hydrology, hydraulics, and hydroclimatology. According to the literature, XGBoost was employed for hydrological modelling, forecasting water quantity and quality, and groundwater management. In the context of hydraulic modelling, the review assessed XGBoost performances for estimating scouring and sediment transport, reservoir modelling, open channel and pressurized flow predictions, and hydraulic structure. Additionally, the role of XGBoost in forecasting hydroclimatic variables, drought assessment, and statistical downscaling was discussed. The review revealed that in 74% of papers, XGBoost or a hybrid XGBoost-based model resulted in the best results among other ML models in diverse applications. Finally, the study presents some suggestions for future studies in the context of XGBoost applications.

中文翻译：

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XGBoost在水资源工程中的应用：系统文献综述（2018年12月-2023年5月）

机器学习方法的应用使水资源工程领域发生了范式转变。这项研究不仅介绍了新兴的极限梯度提升 (XGBoost) 的故事，还全面回顾了 XGBoost 在水文学、水力学和水文气候学问题中的应用。根据文献，XGBoost 用于水文建模、水量和水质预测以及地下水管理。在水力建模方面，该综述评估了 XGBoost 在估算冲刷和泥沙输送、水库建模、明渠和加压流量预测以及水力结构方面的性能。此外，还讨论了 XGBoost 在预测水文气候变量、干旱评估和统计降尺度方面的作用。审查显示，在 74% 的论文中，XGBoost 或基于 XGBoost 的混合模型在不同应用中的其他 ML 模型中取得了最佳结果。最后，该研究为 XGBoost 应用的未来研究提出了一些建议。