Water Demand Forecast and Efficient Supply Management

Abstract

This chapter explores the use of artificial intelligence (AI) models for forecasting water demand and managing water supply systems efficiently. It highlights the need for accurate prediction models in urban areas, where fluctuating water demand poses significant challenges. Various AI techniques, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, are discussed for their ability to handle complex consumption data. The methodology for building these models is also addressed, covering data collection, preparation, and model training and validation. Optimization algorithms, such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), are emphasized to enhance accuracy and robustness. Finally, a practical example illustrates the application of these AI methods in water management, highlighting AI's potential to improve the sustainability of water supply systems and identifying areas for future research to refine these models across various urban settings.