Machine Learning Model for Battle of Water Demand Forecasting - Mario Pagano; Giovanni Francesco Santonastaso; Armando Di Nardo; Salvatore Cuomo; Vincenzo Schiano Di Cola (Engineering proceedings, 2024)
This article investigates the optimization of urban water distribution in the context of population growth and climate change. It highlights the use of the ExtraTreesRegressor algorithm to forecast water demand with greater accuracy. By analyzing a dataset from North-East Italy, the study demonstrates the importance of temporal dynamics over meteorological factors in predicting water consumption patterns. The findings present a novel approach to improving water management strategies, demonstrating machine learning’s potential in addressing critical urban infra-structure challenges.