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

Aifeng Zhai,Xiaowen Ding,Guohe Huang,Guohua Fan

doi:10.3390/w14030463

Aifeng Zhai, Xiaowen Ding + Show 2 more

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https://doi.org/10.3390/w14030463

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Abstract

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.

Highlights

Rainfall–runoff (R–R) modeling plays a very important role in managing the activities of water resources such as flood control and reservoir operation [1,2]
Using the collected data of daily rainfall and water level, multiple linear regression models and artificial neural networks were compared with the Regression Tree Ensemble (RTE) with respect to hydrological forecasting
Fitting results were obtained using this program with a training set and prediction set, before choosing the best one as the predictive factor

Summary

Introduction

Rainfall–runoff (R–R) modeling plays a very important role in managing the activities of water resources such as flood control and reservoir operation [1,2]. Creating and developing effective forecasting tools with high prediction accuracy is becoming urgent because of these complexities and uncertainties. These forecasting tools can typically be divided into two main types: physical and data-driven models. Kuriqi et al [11] investigated the seepage process of Albania in different scenarios using numerical modeling. Data-driven models can directly establish a mathematical relationship between the input and output data with a relatively simpler structure in operation.

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