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Water 2017, 9(11), 880; https://doi.org/10.3390/w9110880

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

1
Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
School of Agricultural Computational and Environmental Sciences, Institute of Agriculture and Environment (IAg&E), University of Southern Queensland, Springfield, QLD 4300, Australia
*
Authors to whom correspondence should be addressed.
Received: 14 October 2017 / Revised: 6 November 2017 / Accepted: 6 November 2017 / Published: 11 November 2017
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Abstract

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. View Full-Text
Keywords: reference evapotranspiration (ET0); extreme-learning machine; support vector regression; ET0 projection; climate change reference evapotranspiration (ET0); extreme-learning machine; support vector regression; ET0 projection; climate change
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yin, Z.; Feng, Q.; Yang, L.; Deo, R.C.; Wen, X.; Si, J.; Xiao, S. Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China. Water 2017, 9, 880.

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