Applications of XGBoost to Water Resource Problems

Dear Colleagues,

With the advancement of soft computational methods, feasible new problem-solving approaches are now at our disposal and enable us to tackle problems in many engineering fields, including water resources. In terms of artificial intelligence (AI), Extreme Gradient Boost (XGBoost) is one of the machine learning (ML) techniques that can be used not only for classification but also for prediction purposes. In essence, it can be classified as an advanced model of Gradient Boost and Decision Tree. It basically consists of a series of leaves developed and connected to one another to serve as an estimation tool. Hence, it can be exploited as a data-driven tool to revisit problems with water resources.

According to the literature, XGBoost is currently being applied to problems regarding the quantity and quality of water resources and hydroclimatic variables. More importantly, such applications are now increasing as XGBoost performs adequately in comparison to data-driven models, particularly when a large amount of data are available for the problem in question. In this regard, this Special Issue intends not only to assess the applicability of XGBoost in comparison with other ML methods but also to collect valuable research into novel applications. Thus, this will be a suitable collection of applications and critical assessments of XGBoost in the domain of water resources for interested researchers and prospective readers. As a result, contributions to this Special Issue are expected to refer to the application of XGBoost and also the evaluation of its performance using rational comparative analysis. Furthermore, comprehensive reviews of the state of the art are welcomed. Finally, topics of interest include (but are not limited to):

• XGBoost applications and assessment in water supply and distribution systems;
• XGBoost applications and assessment in hydrological processes;
• XGBoost applications and assessment in hydraulic engineering;
• XGBoost applications and assessment in river engineering and sediment hydraulics;
• XGBoost applications and assessment in water and climate change;
• Developing XGBoost-based modules to solve problems with water resources

Dr. Majid Niazkar
Dr. Andrea Menapace
Guest Editors

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• XGBoost
• water resources
• machine learning
• artificial intelligence
• data-driven models