Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82214
Title: Predicting Standardized Streamflow index for hydrological drought using machine learning models
Authors: Shamshirband, S
Hashemi, S
Salimi, H
Samadianfard, S
Asadi, E
Shadkani, S
Kargar, K
Mosavi, A
Nabipour, N
Chau, KW 
Issue Date: 2020
Source: Engineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 339-350
Abstract: 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.
Keywords: Gene expression programming
Hydrological drought
M5 model tree
Machine learning models
Standardized streamflow index
Support vector regression
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2020.1715844
Rights: © 2020 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited
The following publication Shahabbodin Shamshirband, Sajjad Hashemi, Hana Salimi, SaeedSamadianfard, Esmaeil Asadi, Sadra Shadkani, Katayoun Kargar, Amir Mosavi, Narjes Nabipour& Kwok-Wing Chau (2020) Predicting Standardized Streamflow index for hydrological droughtusing machine learning models, Engineering Applications of Computational Fluid Mechanics, 14:1,339-350 is available at https://dx.doi.org/10.1080/19942060.2020.1715844
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