基于气象参数的内陆干旱区作物生育期ET<sub>0</sub>预测

魏光辉; 马 亮; 董新光; 杨鹏年

Prediction of ET₀ during growth period of crops in inner arid areas based on meteorological parameters

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DOI：10.7606/j.issn.1000-7601.2014.04.012

Key Words: ET₀ prediction BP neural network (BPNN) grey theory fuzzy least square support vector machine (FLSSVM)

Author Name	Affiliation
WEI Guang-hui, MA Liang, DONG Xin-guang, YANG Peng-nian	(新疆农业大学水利与土木工程学院，新疆乌鲁木齐 830052)

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Abstract:

In order to realize fine irrigation management of field crops, the ET₀ prediction model was studied based on meteorological parameters in growth period. The grey theory was applied to analyze the grey relational degree between meteorological factors (daily average temperature, maximu m temperature, minimum temperature, wind speed, relative humidity and sunshine hours) and ET₀. The results showed that the grey correlation degree between ET₀ and temperature (including daily average, maximum and minimum temperature) as well as relative humidity was relatively high. Based on the correlation coefficients between ET₀ and meteorological parameters, the BP neural network (BPNN) prediction model was established by using daily average temperature, wind speed and sunshine hours as input and ET₀ as output of the model. The fuzzy least square support vector machine (FLSSVM) prediction model was also established by using daily average temperature, wind speed, sunshine hours and grey correlation degree as input. The results showed that: in the BPNN model, the trained determination coefficient was 0.8643 with the average relative error of 6.29%, and the predicted determination coefficient was 0.8099 with the average relative error of 7.83%; in the FLSSVM model, the trained determination coefficient was 0.9684 with the average relative error of 2.89%, and the predicted determination coefficient was 0.9663 with the average relative error of 3.43%. Both BP neural network and FLSSVM model exhibited a high precision and could be used to predict daily value of ET₀. The results could provide the theoretical and technical support for the fine irrigation management of field crops.