Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling

Autor: Potharlanka Jhansi Lakshmi, Rubén Apaza Apaza, Ahmed Alkhayyat, Haydar Abdulameer Marhoon, Ameer A. Alameri
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Water Science and Technology, Vol 86, Iss 12, Pp 3205-3222 (2022)
Druh dokumentu: article
ISSN: 0273-1223
1996-9732
DOI: 10.2166/wst.2022.400
Popis: It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as support vector machine (SVM), gene expression programming (GEP), wavelet-SVM (WSVM), and wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River Basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two different types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with wavelet improved simulation accuracy, which is now the highest for the WGEP method. HIGHLIGHTS The accuracy of machine learning based approaches for rainfall-runoff modeling is evaluated in the Yellow River Basin in China.; Wavelet transform as a pre-processing tool is combined with Gene Expression Programming and Support Vector Machine.; The combination of Wavelet and Gene Expression Programming (WGEP) performed best.;
Databáze: Directory of Open Access Journals
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