Artificial neural network models for estimating regional reference evapotranspiration based on climate factors

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	Artificial neural network models for estimating regional reference evapotranspiration based on climate factors
	Li Yunsheng; Dai Xiaoqin
	2009
关键词	Arid regions Atmospheric humidity Backpropagation Errors Evapotranspiration Irrigation Water supply
英文摘要	Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, an artificial neural network (ANN) model for reference evapotranspiration (ET0) calculation was investigated. ANNs were trained and tested for arid (west), semi-arid (middle) and sub-humid (east) areas of the Inner Mongolia district of China. Three or four climate factors, i.e. air temperature (T), relative humidity (RH), wind speed (U) and duration of sunshine (N) from 135 meteorological stations distributed throughout the study area, were used as the inputs of the ANNs. A comparison was conducted between the estimates provided by the ANNs and by multilinear regression (MLR). The results showed that ANNs using the climatic data successfully estimated ET0 and the ANNs simulated ET0 better than the MLRs. The ANNs with four inputs were more accurate than those with three inputs. The errors of the ANNs with four inputs were lower (with RMSE of 0.130 mm d^-1, RE of 2.7% and R² of 0.986) in the semi-arid area than in the other two areas, but the errors of the ANNs with three inputs were lower in the sub-humid area (with RMSE of 0.21 mm d^-1, RE of 5.2% and R² of 0.961. For the different seasons, the results indicated that the highest errors occurred in September and the lowest in April for the ANNs with four inputs. Similarly, the errors were higher in September for the ANNs with three inputs. Copyright © 2008 John Wiley & Sons, Ltd.
出处	Hydrological Processes
卷	23 期:3 页:442-450
收录类别	EI
语种	英语
内容类型	EI期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/24970]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Li Yunsheng,Dai Xiaoqin. Artificial neural network models for estimating regional reference evapotranspiration based on climate factors. 2009.