Research of Soil Moisture Content Forecast Model Based on Genetic Algorithm BP Neural Network

Abstract : Soil moisture forecast model based on genetic neural network is established because the soil moisture forecasting is nonlinear and complex. The weights and threshold value of BP network are optimized according to the total situation optimization ability of genetic algorithm, which can avoid effectively that BP network is vulnerable to run into the local minimum value as its poor total optimization ability. The model is applied to Hongxing farm in Heilongjiang Province to predict the soil moisture. The forecasting result shows that the model has favorable forecasting precision, which indicates that the genetic neural network model is feasible and effective to predict the soil moisture.
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Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-345 (Part II), pp.309-316, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18336-2_37〉
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Caojun Huang, Lin Li, Souhua Ren, Zhisheng Zhou. Research of Soil Moisture Content Forecast Model Based on Genetic Algorithm BP Neural Network. Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-345 (Part II), pp.309-316, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18336-2_37〉. 〈hal-01562766〉

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