Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous inland watershed in north-west China
Yin, Zhenliang and Feng, Qi and Yang, Linshan and Deo, Ravinesh C. and Wen, Xiaohu and Si, Jianhua and Xiao, Shengchun (2017) Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous inland watershed in north-west China. Water, 9 (11). pp. 880-902.
Text (Published Version)
water-09-00880.pdf
Official URL: http://www.mdpi.com/2073-4441/9/11/880
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) outputs retrieved from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) were employed to downscale monthly ET0 for the historical period 1960–2005 as a validation approach and for the future period 2010–2099 as a projection of ET0 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The following conclusions can be drawn: the ELM and SVR methods demonstrate a very good performance in estimating Food and Agriculture Organization (FAO)-56 Penman–Monteith ET0. Variation in future ET0 mainly occurs in the spring and autumn seasons, while the summer and winter ET0 changes are moderately small. Annually, the ET0 values were shown to increase at a rate of approximately 7.5 mm, 7.5 mm, 0.0 mm (8.2 mm, 15.0 mm, 15.0 mm) decade−1, respectively, for the near-term projection (2010–2039), mid-term projection (2040–2069), and long-term projection (2070–2099) under the RCP4.5 (RCP8.5) scenario. Compared to the historical period, the relative changes in ET0 were found to be approximately 2%, 5% and 6% (2%, 7% and 13%), during the near, mid- and long-term periods, respectively, under the RCP4.5 (RCP8.5) warming scenarios. In accordance with the analyses, we aver that the opportunity to downscale monthly ET0 with artificial intelligence is useful in practice for water-management policies
Article (Commonwealth Reporting Category C)
Item Status:
Live Archive
Published version made accessible under a Creative Commons Attribution License 4.0. This study was supported by Chinese Academy of Science President's Fellowship Program 2016 awarded to Dr Ravinesh Deo.
Faculty/School / Institute/Centre:
Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 July 2013 - 5 Sept 2019)
reference evapotranspiration (ET0); extreme-learning machine; support vector regression; ET0 projection; climate change
Fields of Research :
05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
Socio-Economic Objective:
E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
10.3390/w9110880
http://eprints.usq.edu.au/id/eprint/33427
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