期刊
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
卷 14, 期 1, 页码 339-350出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/19942060.2020.1715844
关键词
Gene expression Programming; hydrological drought; M5 model tree; machine learning models; Standardized Streamflow index; support vector regression
Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling the droughts. In this study, three indices of drought, i.e., Standardized Precipitation Index (SPI), Standardized Streamflow Index (SSI), and Standardized Precipitation Evapotranspiration Index (SPEI), are modeled using Support Vector Regression (SVR), Gene Expression Programming (GEP), and M5 model trees (MT). The results indicate that SPI delivered higher accuracy. Moreover, MT model performed better in predicting SSI by a CC of 0.8195 and a RMSE of 0.8186.
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