Flow interval prediction based on deep residual network and lower and upper boundary estimation method

Abstract

Interval prediction is an efficient approach for quantifying the uncertainties of future flow. In this paper, a novel method, based on the combination of a deep residual neural network (ResNet) and lower and upper bound estimation (LUBE), is proposed to forecast future flow and construct prediction intervals. LUBE is proposed by optimizing a LUBE-based objective function and adjusting the type and quantity of residual blocks to design a combined residual network. The final proposed interval prediction model is stResNet-LUBEproposed. The performance of the stResNet-LUBEproposed model is verified using the spatiotemporal dataset of Tunxi, which is a small and medium watershed. The performance of proposed model is mainly evaluated by the root mean square error (RMSE), coefficient of determination (R2), and coverage width-based criterion (CWC). The experimental results show that the average values of RMSE, R2, and CWC of the proposed model are better than those of the spatiotemporal deep learning model stCNN-LUBEproposed, the deep learning model LSTM-LUBEproposed, and the machine learning model MLP-LUBEproposed by 1.881%, 10.574%, and 14.113%; 2.198%, 15.754%, and 18.546%; and 10.572%, 35.907%, and 46.819%, respectively (predict 6-step).