Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China

Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China

This study aims to project future variability of reference evapotranspiration (ET0) using artificial intelligence methods, constructed with an extreme-learning machine (ELM) and support vector regression (SVR) in a mountainous inland watershed in north-west China. Eight global climate model (GCM) ou...

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Bibliographic Details
Main Authors: Zhenliang Yin, Qi Feng, Linshan Yang, Ravinesh C. Deo, Xiaohu Wen, Jianhua Si, Shengchun Xiao
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:Water
Subjects:
reference evapotranspiration (ET0)
extreme-learning machine
support vector regression
ET0 projection
climate change
Online Access:https://www.mdpi.com/2073-4441/9/11/880

Internet

https://www.mdpi.com/2073-4441/9/11/880

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