Numerical Simulation of Evaporation of Arid Region Based on Extreme Learning Machine

doi:10.7522/j.issn.1000-694X.2017.00097

JOURNAL OF DESERT RESEARCH ›› 2017, Vol. 37 ›› Issue (6): 1219-1226.DOI: 10.7522/j.issn.1000-694X.2017.00097

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Numerical Simulation of Evaporation of Arid Region Based on Extreme Learning Machine

Wang Tingting^1,2,3, Feng Qi¹, Wen Xiaohu¹, Guo Xiaoyan¹

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 Economics & Management, Lanzhou Jiaotong University, Lanzhou 730000, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2017-06-15 Revised:2017-09-20 Online:2017-11-20 Published:2017-11-20

Abstract

Abstract: The simulated accurately evaporation at the arid regions was essential to rationally develop and utilize water resources and the ecosystem protection. The meteorological data at Wushaoling and Gulang weather stations in Shiyang River Basin were used in this study. Based on the extreme learning machine (ELM) model, the varying combination of meteorological factors were inputted to the model. An evaporation model also was established to simulate monthly evaporation at the two weather stations, and the results were compared with support vector machine (SVM) model to evaluate the simulation ability of ELM model. Our study demonstrated that the ELM model had better applicability in simulating monthly evaporation at arid regions. It can provide a new method and idea for calculating evaporation, and it is a desirable and effective method to calculate evaporation at the arid regions with insufficient data.

Key words: arid region, evaporation, numerical simulation, extreme learning machine

CLC Number:

P332.2

Wang Tingting, Feng Qi, Wen Xiaohu, Guo Xiaoyan. Numerical Simulation of Evaporation of Arid Region Based on Extreme Learning Machine[J]. JOURNAL OF DESERT RESEARCH, 2017, 37(6): 1219-1226.

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Numerical Simulation of Evaporation of Arid Region Based on Extreme Learning Machine

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