Application of backpropagation neural network to estimate evapotranspiration for ChiaNan Irrigated Area, Taiwan
Kuo, Sheng-Feng, author
Tsai, Ming-Hua, author
Lin, Wei-Taw, author
Ho, Yi-Fong, author
U.S. Committee on Irrigation and Drainage, publisher
Colorado State University. Libraries
Backpropagation Neural Network is applied to establish the relationship between meteorological factors and evapotranspiration, which is then used to predict the evapotranspiration in ChiaNan irrigated area, Taiwan. It takes the weather data from Irrigation Experiment Station of ChiaNan Irrigation Association as the input layer, which include the following weather factors: (1) the highest temperature; (2) the lowest temperature; (3) average temperature; (4) relative humidity; (5) wind speed; (6) sunlight hours; (7) solar radiation amount; (8) dew point; (9) forenoon ground temperature; (10) afternoon ground temperature. From the result it can be known that the correlation coefficient reaches 0.993 between the evapotranspiration in 2004 calculated by FAO56 Penman-Monteith method and the one predicted by the neural network model with a hidden layer of 10 nodes. The actual evapotranspiration is 911.6cm and the prediction by the neural network is 864.4, between which the error ratio is 1.67%. The correlation coefficient is 0.708 between the actual evaporation in 2004 and the prediction by the neural network with a hidden layer of 10 nodes and an output layer with the pan evaporation as its target output. The pan evaporation is 1674.1cm, while the prediction by the neural network is 1451.7cm, between which the error ratio is 13.23%.
Presented at the Role of irrigation and drainage in a sustainable future: USCID fourth international conference on irrigation and drainage on October 3-6, 2007 in Sacramento, California.