Regression Tree Ensemble Rainfall-Runoff Forecasting Model and Its Application to Xiangxi River, China

The development of an efficient and accurate hydrological forecasting model is essential for water management and flood control. In this study, the ensemble model was applied to predict the daily discharge; it not only could enhance the algorithm and improve the learning accuracy, but it was also the most effective representative model among various combinations of learning parameters. Using the survey data of Xingshan station in Xiangxi River, China, the suitability of the model was proven. The performance of the ensemble model was compared with the multiple linear regression model and the artificial neural network models. Furthermore, the length of the training samples and the peak value predictions were analyzed. The results showed that, firstly, the best effect of the discharge simulation model appeared in the ensemble model, while the simulation accuracy of the multiple linear regression model was lower than that of the artificial neural network model in some cases. Secondly, the prediction effect of the ensemble model for discharge was better than that of the single model to some extent, whereby the maximum absolute value of relative error was 8.11% using the ensemble model. A comprehensive analysis showed that the ensemble model was optimal. Furthermore, the ensemble model performed outstandingly in terms of hydrological forecasting. The ensemble model also provided theoretical support for hydrological forecasting and could be considered as an alternative to multiple linear regression models and artificial neural networks.

Automated summary

Efficient and accurate hydrological forecasting model is crucial for water management and flood control. This study applied the ensemble model to predict daily discharge and proved its suitability using survey data. Compared with multiple linear regression and artificial neural network models, the ensemble model showed better performance in discharge simulation and prediction.