Applications of machine learning to water resources management: A review of present status and future opportunities

作者：Ashraf A. Ahmed, Sakina Sayed, Antoifi Abdoulhalik, Salissou Moutari, Lukumon O. Oyedele · 发表于：Journal of Cleaner Production · 年份：2024 · DOI：10.1016/j.jclepro.2024.140715 · 被引用次数：253 · 研究领域：Hydrological Forecasting Using AI、Water resources management and optimization、Flood Risk Assessment and Management

Water is the most valuable natural resource on earth that plays a critical role in the socio-economic development of humans worldwide. Water is used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at a global scale, coupled with the predicted climate change-induced impacts, warrants the need for proactive and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in water-related fields, this review paper presents for the first time a comprehensive review of machine learning techniques applied to water resources management, focusing on the most recent achievements. The study examines the potential for advanced machine learning techniques to improve decision support systems in the various sectors within the realm of water resources management, which includes groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, water drainage systems, and flood management and defence. This study provides an overview of the state-of-the-art machine learning approaches to the water industry and how they can be used to ensure water supply sustainabil...