Comparing Predictive Machine Learning Models for Short- and Long-Term Urban Water Demand Forecasting in Milan, Italy
Wenjin Hao, Andrea Cominola, Andrea Castelletti
Abstract
Urban water demand forecasting is essential for water supply network optimization and management. In this case study, we comparatively investigate different state-of-the-art predictive models on short- (1 day-ahead) and long-term (7 day-ahead) urban water demand (UWD) forecasting for the city of Milan, Italy. The contribution of this paper is two-fold. First, we compare the forecasting performance of different time series and machine learning models on daily UWD. The tested models include Autoregressive Integrated Moving Average (ARIMA) models, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory (LSTM) networks. Second, we investigate whether coupling a Wavelet Data-Driven Forecasting Framework (WDDFF) with these models further improves predictive capacity. Results show that, in general, WDDFF can improve model predictive performance. LSTM coupled wavelet decomposition technique can achieve high levels of accuracy with R2 larger than 0.9 for both short- and long-term UWD forecasts. LightGBM can efficiently reduce the number of predictors and show the potential to forecast and select important features in the hydrology and water resources field.