Reservoir inflow predicting model based on machine learning algorithm via multi‐model fusion: A case study of Jinshuitan river basin

Abstract

Abstract Flood prevention and disaster mitigation have a great impact on people's lives and properties, and so it is urgent to realise high‐accuracy inflow predictions for flood early warning. To this end, a prediction model based on a machine learning algorithm via a multimodel combination method is proposed to predict the inflow of Jinshuitan reservoir. Firstly, a data formatting scheme called the ’hydrological regime profile‘ is designed for input data. The whole data set is partitioned into a low‐flow subset and a high‐flow subset. Considering the high dimensions of the complex input data, convolutional neural networks (CNN), EXtreme gradient Boosting model (XGBoost) and a partial least squares model (PLS) are used. In the CNN and XGBoost models, a special loss function weighted on inflow is designed to improve the performance on high‐inflow predictions. Finally, a multi‐model combination method is proposed to improve the prediction performance. Compared with XGBoost, CNN and PLS, the root mean square error of the combined model is reduced by 41.64%, 72.29% and 3.41%, respectively. As a consequence, the combined model is able to predict the inflows with higher accuracy compared to the single models.

Keywords

• disasters
• floods
• geophysics computing
• hydrological techniques
• hydrology
• learning (artificial intelligence)