Journal
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS
Volume 14, Issue 1, Pages 339-350Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/19942060.2020.1715844
Keywords
Gene expression Programming; hydrological drought; M5 model tree; machine learning models; Standardized Streamflow index; support vector regression
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available