A Hybrid LSSVM Model with Empirical Mode Decomposition and Differential Evolution for Forecasting Monthly Precipitation

Abstract

In this study, a hybrid least squares support vector machine (HLSSVM) model is presented for effectively forecasting monthly precipitation. The hybrid method is designed by incorporating the empirical mode decomposition (EMD) for data preprocessing, partial information (PI) algorithm for input identification, and differential evolution (DE) for model parameter optimization into least squares support vector machine (LSSVM). The HLSSVM model is examined by forecasting monthly precipitation at 138 rain gauge stations in the Yangtze River basin and compared with the LSSVM and LSSVM–DE. The LSSVM–DE is built by combining the LSSVM and DE. Two statistical measures, Nash–Sutcliffe efficiency (NSE) and relative absolute error (RAE), are employed to evaluate the performance of the models. The comparison of results shows that the LSSVM–DE gets a superior performance to LSSVM, and the HLSSVM provides the best performance among the three models for monthly precipitation forecasts. Meanwhile, it is also observed that all the models exhibit significant spatial variability in forecast performance. The prediction is most skillful in the western and northwestern regions of the basin. In contrast, the prediction skill in the eastern and southeastern regions is generally low, which shows a strong relationship with the randomness of precipitation. Compared to LSSVM and LSSVM–DE, the proposed HLSSVM model gives a more significant improvement for most of the stations in the eastern and southeastern regions with higher randomness.

Multiscale teleconnection analysis of monthly total and extreme precipitations in the Yangtze River Basin using ensemble empirical mode decomposition

“…In general, local precipitation is influenced by complex interactions of ocean, atmosphere, and land surface processes, which leads to the result that the physical mechanisms for change in precipitation are complicated (O'Gorman and Schneider, 2009; Tao et al ., 2017). However, the climate indices combining information about the oceans and the atmosphere can provide key information about long‐term precipitation, so they have been recognized as the most important factors influencing on the generation of extreme precipitation (Wang and Yan, 2011; Wang and Yang, 2016; Limsakul and Singhruck, 2016; Kim et al ., 2018; Tong et al ., 2019; Jiang et al ., 2019; Chen et al ., 2019).…”

“…The multi‐resolution analysis methods including wavelet transform (WT) and empirical mode decomposition (EMD) have been widely used to decompose the original time series into various simpler components to extract meaningful information (Napolitano et al ., 2011; He et al ., 2014; Tao et al ., 2017). However, the application of WT demands the choice of basis functions, requiring considerable skill in their selection and application (Huang et al ., 2014; Tao et al ., 2017). The EMD as another non‐stationary data analysis method is based on the principle of local scale separation and does not require any predetermined basis functions (Karthikeyan and Nagesh Kumar, 2013).…”

A Forecasting Model on Carrying Capacity for Government's Controlling Measure Under Environmental Law in Thailand: Adapting Non-Recursive Autoregression Based on the Var-X Model

“…This method has also been used to solve game theory optimal projection (GTOP). Furthermore, Tao et al (2017) forecasted monthly precipitation at 138 rain gauge stations in the Yangtze River basin by implementing a hybrid least squares support vector machine (HLSSVM) model. At the same time, the LSSVM-DE was built to provide a comparison tool by combining the LSSVM and differential evolution (DE).…”

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms

“…Therefore, this study aims to develop a high-performance hybrid machine learning model for modeling the Mr using LSSVM. Although the hybrid LSSVM models outperform single LSSVM and SVM, the method in modeling nonlinear problems 32 , its use in modeling Mr is still limited. Hybrid LSSVM was applied in different engineering applications, and that performance is shown to be high 24 , 32 – 34 .…”

“…Although the hybrid LSSVM models outperform single LSSVM and SVM, the method in modeling nonlinear problems 32 , its use in modeling Mr is still limited. Hybrid LSSVM was applied in different engineering applications, and that performance is shown to be high 24 , 32 – 34 . For instance, LSSVM- particle swarm optimization (PSO) was proposed to model slope stability, and the results showed that the performance of the model was high 35 .…”