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

Aug 3, 2026

Water is a critical natural resource for socio-economic development, supporting drinking, recreation, irrigation, and energy generation. Growing population pressure and climate change impacts make proactive water resources management essential for ensuring supply reliability, quality, and resilience to floods and droughts. This review consolidates recent machine learning methods applied across groundwater, forecasting, water distribution, water quality, wastewater treatment, demand management, hydropower and marine energy, drainage, and flood risk. Findings highlight strong performance from LSTM and hybrid approaches, supporting improved decision support, optimization, and sustainability outcomes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Introduction

Water resources management needs and challenges

Population growth and climate change pressures

Flooding and water scarcity drivers

Machine learning applications across water sectors

Groundwater management and streamflow forecasting

Water distribution, water quality, and wastewater treatment

Demand, hydropower/marine energy, drainage, and flood defence

Journal of Cleaner Production xxx (xxxx) 140715 Contents lists available at ScienceDirect Journal of Cleaner Production [journal homepage: www.elsevier.com/locate/jclepro](journal homepage: www.elsevier.com/locate/jclepro) | Review
Applications of machine learning to water resources management: A review of present status and future opportunities
Ashraf A. Ahmed a, **, Sakina Sayed a, Antoifi Abdoulhalik a, Salissou Moutarib, Lukumon Oyedele c
a Department of Civil and Environmental Engineering, Brunel University London, Kingston Lane, Uxbridge, UB83PH, United Kingdom b Mathematical Sciences Research Centre, Queen's University Belfast, United Kingdom
c Bristol Business School, University of West of England, Bristol, BS16 1QY, United Kingdom | | | --- | --- | | A R T I C L E I N F O
Handling Editor: Cecilia Maria Villas Bôas de Almeida
Keywords:
Water quality Flooding
Wastewater treatment AI and deep learning Reinforcement learning Unsupervised learning | A B S T R A C T
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 mostrecent 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 sustainability, quality, and flood and drought mitigation. This review covers the most recent related studies to provide the most recent snapshot of machine learning applications in the water industry. Overall, LSTM networks have been proven to exhibit reliable performance, often outperforming ANN models, traditional machine learning models, and established physicsbased models. Hybrid ML techniques have exhibited great forecasting accuracy across all water-related fields, often showing superior computational power over traditional ANNs architectures. In addition to purely data-driven models, physical-based hybrid models have also been developed to improve prediction performance. These efforts further demonstrate that Machine learning can be a powerful practical tool for water resources management. It provides insights, predictions, and optimisation capabilities to help enhance sustainable water use and management and improve socio-economic development, healthy ecosystems and human existence. | 1. Introduction Water is the most essential natural resource for human life that is used in various ways, which are keys for human socio-economic development. Water is used for drinking, bathing, recreational activities, agriculture, hydropower production, and more. Although water covers around 70% of the earth's surface, only about 2.5% is freshwater (Science Daily, 2020). Therefore, appropriate water resources management is crucial to a well-developed society. As a complex sy

FAQs

What problem does the review address in water resources management?

It addresses the need for proactive and effective management of water resources under population growth and climate change pressures, including water scarcity and flooding risk.

Which machine learning areas within water management does the review cover?

It covers groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, drainage systems, and flood management and defence.

What approaches does the review identify as performing well?

The review highlights reliable performance of LSTM networks and strong forecasting accuracy from hybrid ML techniques, with hybrid physical-based models also improving prediction outcomes.

Aug 3, 2026

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