A Methodology for Forecasting Demands in a Water Distribution Network Based on the Classical and Neural Networks Approach

This paper proposes a three (3)-step methodology to forecast the future water demands of a water distribution network (WDN) composed of ten (10) district metered areas (DMAs). First, pre-processing of the time-series data was performed through outlier elimination, imputation by K-Nearest Neighbors (KNN), and statistical data scaling. Second, the model hyperparameters were calibrated using Bayesian optimization. Third, Long Short-Term Memory (LSTM) coded as a Multi-Step Multivariate Time-Series forecasting model was implemented. Our results indicate that the proposed model produces accurate future water demands, suggesting that feasible short-term water demand forecasting models require combining engineering judgment and computational tools to achieve reliability.