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Optimising Water Use Through Smart Models and Artificial Intelligence
Abstract
This study examines the use of advanced AI techniques to optimize water management in three key areas: water quality prediction, leak detection, and water distribution. By applying ensemble learning models like Random Forest, Gradient Boosting, AdaBoost, and Bagging, the research addresses the complexities of managing water resources, such as non-linear data patterns and the need for high predictive accuracy. Integrating AI models with real-time data and IoT technologies enhances the adaptability of water management, enabling real-time monitoring and decision-making for efficient and sustainable resource use. These AI-driven approaches improve operational efficiency by optimizing water distribution and minimizing losses from leaks while ensuring accurate water quality predictions. This contributes to better decision-making, crucial for public health and environmental sustainability. The study highlights the transformative potential of AI in water management, advocating for its broader adoption to meet the challenges posed by urbanization, population growth, and climate change.
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