A systematic review and meta-analysis of groundwater level forecasting with machine learning techniques: Current status and future directions

Uc-Castillo, José Luis; Marín-Celestino, Ana Elizabeth; Martínez-Cruz, Diego Armando; Tuxpan, José; Ramos-Leal, José Alfredo

doi:10.1016/j.envsoft.2023.105788

Cited by 9 publications

(1 citation statement)

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“…In water resource management, such data enable a more comprehensive consideration of problems and the establishment of improved decision-making models [17]. Consequently, machine learning, owing to its robust data mining capabilities, is widely applied in areas like water resource supply and demand prediction, flood risk management, water quality monitoring, and forecasting scenarios [18][19][20][21][22]. Researchers have reviewed the application of machine learning in water resource modeling, laying the foundation for further in-depth research [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Research on Water Resource Modeling Based on Machine Learning Technologies

Liu,

Zhou,

Yang

et al. 2024

Water

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Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological observation methods, often reliant on experience and statistical methods, are time-consuming and labor-intensive, frequently resulting in predictions of limited accuracy. However, machine learning technologies enhance the efficiency and sustainability of water resource modeling by analyzing extensive hydrogeological data, thereby improving predictions and optimizing water resource utilization and allocation. This review investigates the application of machine learning for predicting various aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. It provides a detailed summary of various algorithms, examines their technical strengths and weaknesses, and discusses their potential applications in water resource modeling. Finally, this paper anticipates future development trends in the application of machine learning to water resource modeling.

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