Short-Term Water Demand Forecasting Using Machine Learning Approaches in a Case Study of a Water Distribution Network Located in Italy

Abstract

Machine learning’s application in short-term water demand forecasting remains a pivotal area of research in water distribution system studies. This investigation reveals a distinctive distribution pattern for the daily demand following dataset preprocessing with Random Forest and the quartile method. Inspired by the findings, this study introduces a novel Water Demand Forecast Framework (WDFF) using DMA characteristics and the CNN–Attention–LSTM architecture. By analyzing the relationship between the total and DMA-specific demand, the WDFF is found to enhance the predictions. It demonstrates expedited convergence and reduces the loss metric, demonstrating its potential to elevate the predictive precision in water demand forecasting.

Keywords

• short-term water demand forecasting
• time series
• feature engineering
• convolutional neural network
• long short-term memory network
• attention mechanism