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Application of Artificial Intelligence and Machine Learning Algorithms to Predict Water Demand and Optimise Resource Allocation in Arid Regions: A Narrative Review

Saadu Umar Wali, Abdullahi Bala Usman, Umar Abdullahi, Ibrahim Umar, Mohammed, and Jamil Musa Hayatu

Abstract

In arid regions where water scarcity severely impacts sustainable development and livelihoods, Artificial Intelligence (AI) and Machine Learning (ML) offer data-driven solutions to enhance water resource management. This narrative review critically examines the application of specific AI/ML techniques—such as Random Forest, Gradient Boosting Machines (GBM), and Long Short-Term Memory (LSTM) networks—for forecasting short- and long-term water demand under dynamic climatic and socio-economic conditions. Clustering algorithms like K- means and DBSCAN are reviewed for their role in user segmentation and pattern recognition, while isolation forests and autoencoders are explored for anomaly detection in identifying leakages and irregular consumption. Reinforcement learning models, including Deep Q- Networks (DQN) and Proximal Policy Optimization (PPO), are assessed for real-time water allocation and adaptive control strategies. The review contrasts these intelligent systems with conventional methods, emphasizing the improved precision, adaptability, and predictive capacity of AI-driven approaches. It concludes by offering insights for building climate- resilient, efficient, and equitable water management systems in arid environments.

Keywords

Artificial Intelligence (AI)Machine Learning (ML)Water Demand PredictionConservation Strategies

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