A Deep Learning Model with Attention-BiLSTM Networks Combining XGBoost Residual Correction for Short-Term Water Demand Forecast

As a key component of water distribution management, reliable short-term water demand forecasting plays a fundamental role in the optimal control for water supply. Most reported approaches based on deep learning omit the two-way information flow existed in historical water demand data and the model inputs cannot automatically highlight the significance of crucial features to current water demands, which could have impact on the prediction accuracy. Owing to the high nonlinear changes and fluctuations in water demand series, making accurate forecast a challenging task. To address the problem in this study, maximal information coefficient (MIC) is presented for feature extraction analysis, deep learning with Attention-BiLSTM networks is developed, to reinforce the performance, that combining the XGBoost algorithm as a residual correction module to forecast short-term water demand. Hyper-parameter configurations are conducted with the models. Finally, the superiority of proposed method is illustrated by comparing to other benchmark models. The results show that the proposed method outperforms other predictive models, in which both on the accuracy and stability.