Forecasting Short-Term Water Demands with an Ensemble deep learning Model for a Water Supply System

Abstract

Abstract Short-term water demand forecasting is crucial for constructing intelligent water supply system. There are plenty of useful models built to address this issue. However, there are still many challenging problems, including that the accuracies of the models are not high enough, the complexity of the models makes them hard for wide use in reality and the capabilities of models to catch peaks still have much room for improvement. In order to solve these problems, we proposed an ensemble deep learning model named STL-Ada-LSTM for daily water demand forecast by combining STL method with AdaBoost-LSTM model. After data preprocessing, the smoothed series is decomposed by STL to gain three input series. Then, several LSTM models are integrated by the AdaBoost algorithm to construct the ensemble deep learning model for water demand forecast. At last, the superiority of the proposed model is demonstrated by comparing with other state-of-art models. The proposed method is applied for water demand forecast using daily datasets from two representative water plants in Yiwu, East China. All models are assessed by mean absolute scaled error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R2) and Akaike information criterion (AIC). The results show that the proposed model not only enhances the accuracy of the forecast, but also improves the stability and conciseness, which make it a practical daily water demand forecast model.