Predicting Net Inflow for 10 DMAs in North-East Italy

This paper introduces a two-step methodology for short-term water demand forecasting.In the first step, a pre-processing analysis of the inflow input data is conducted to evaluate completeness and quality, ensuring optimal data integrity.Subsequently, in the second step, a robust machinelearning algorithm is employed to predict the water demand patterns.The methodology is applied across 10 District Metering Areas (DMAs) in the north-east of Italy, each characterized by unique demographic features.Accordingly, tailored features are carefully selected for inclusion in the water demand forecast for each DMA.