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《Water Saving Irrigation》 2016-10
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BP Prediction Model for Soil Wilting Coefficient Based on Principal Component Analysis

YU Jin;FAN Gui-sheng;College of Water Resource Science and Engineering,Taiyuan University of Technology;  
Based on the test data of farming soil wilting percentage of farmland in the Loess Plateau region,aprediction model for soil wilting coefficient is established based on the combination of principal component analysis and BP neural network.Through principal component analysis,the number of neurons in the input layer is reduced,the network structure is optimized and the work efficiency is enhanced.The mean relative error between the predicted value and the measured value is within 5% and in an acceptable range,which indicates that using the soil basic physicochemical parameters to forecast wilting percentage of farming soil is feasible.The study results not only improve the forecasting accuracy and convergence speed of traditional neural networks,but also provide a strong theoretical support for the crop water management and promoting soil production potential in the Loess Plateau.
【Fund】: 国家自然科学基金项目(40671081)
【CateGory Index】: S152.7
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