Water Demand Forecasting with Multi-Objective Computational Intelligence †

Abstract

With increasing pressures from population growth, urbanization, and climate change, effective water resource management is crucial. This paper presents a computational intelligence framework employing machine learning and multi-objective optimization for the short-term forecasting battle of urban water demand within District Metered Areas (DMAs). Our methodology utilizes historical data from DMAs in North-East Italy, focusing on daily and weekly forecasts to optimize water utility operations and energy purchasing. By integrating environmental variables, the proposed models aim to improve forecasting accuracy, model interpretability, and structural complexity, thus meeting the practical needs of water utilities.