A novel medium- and long-term runoff combined forecasting model based on different lag periods

Ping Ai,Yanhong Song,Chuansheng Xiong,Binbin Chen,Zhaoxin Yue

doi:10.2166/hydro.2022.116

Abstract

Abstract The accuracy of medium- and long-term runoff forecasting plays a signiﬁcant role in several applications involving the management of hydrological resources, such as power generation, water supply and ﬂood mitigation. Numerous studies that adopted combined forecasting models to enhance runoff forecasting accuracy have been proposed. Nevertheless, some models do not take into account the effects of different lag periods on the selection of input factors. Based on this, this paper proposed a novel medium- and long-term runoff combined forecasting model based on different lag periods. In this approach, the factors are initially selected by the time-delay correlation analysis method of different lag periods and further screened with stepwise regression analysis. Next, an extreme learning machine (ELM) is adopted to integrate each result obtained from the three single models, including multiple linear regression (MLR), feed-forward back propagation-neural network (FFBP-NN) and support vector regression (SVR), which is optimized by particle swarm optimization (PSO). To verify the effectiveness and versatility of the proposed combined model, the Lianghekou and Jinping hydrological stations from the Yalong River basin, China, are utilized as case studies. The experimental results indicate that compared with MLR, FFBP-NN, SVR and ridge regression (RR), the proposed combined model can better improve the accuracy of medium- and long-term runoff forecasting in the statistical indices of MAE, MAPE, RMSE, DC, U95 and reliability.

Highlights

With the development of national economy and the adjustment of national water control policy, the gap between the existing hydrologic medium- and long-term runoff forecasting methods and the demand for production and application have been further widened
At last, considering that the random input weights and hidden biases of extreme learning machine (ELM) always have some influence on the training process, we use particle swarm optimization (PSO) to optimize the parameters to derive the resultant forecast
It is of great significance to develop medium- and long-term runoff forecasting for water resource planning and management activities such as water conservancy infrastructure operation, flood control, reservoir operation and drinking water distribution

Summary

Introduction

With the development of national economy and the adjustment of national water control policy, the gap between the existing hydrologic medium- and long-term runoff forecasting methods and the demand for production and application have been further widened. Due to the increasing amount of hydrological data, it can introduce redundant and noisy information to the prediction feature or factor, which may deteriorate the performance of the mid- to long-term runoff prediction (Yue et al 2020a). The models to predict medium- and long-term runoff fall into two main categories: process-driven models and data-driven models. The process-driven model is based on the conception of hydrology, with which the discharge forecasting can be performed by simulation of the runoff variation and river channel evolution. The data-driven model can make the best use of existing data to achieve a predetermined model structure (Lu et al 2021)

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Conclusion