Saturday 23rd of September 2017
 

Research on Spatial Estimation of Soil Property Based on Improved RBF Neural Network


Jianbo Xu, Quanyuan Tan, Lisheng Song, Kai Hao and Ke Xiao

To seek optimal network parameters of Radial Basis Function (RBF) Neural Network and improve the accuracy of this method on estimation of soil property space, this study utilizes genetic algorithm to optimize three network parameters of RBF Neural Network including the number of hidden layer nodes, expansion speed and root-mean-square error. Then, based on optimized RBF Neural Network, spatial interpolation is conducted for arable soil property under different sampling scales in the study area. The estimation result is superior to RBF Neural Network method without optimization and geostatistical method in terms of the fitting capacity and interpolation accuracy. Compared with the result of space estimation by RBF Neural Network method without optimization, among the 5 schemes, the forecast errors of RBF Neural Network optimized by genetic algorithm reduce greatly. Mean absolute error (MAE) reduces 0.4868 on the average and root-mean-square error (RMSE) reduces 1.492 on the average. Therefore, RBF Neural Network method optimized by genetic algorithm can gain the information about regional soil property spatial variation more accurately and provides technical support for arable land quality evaluation, accurate farmland management and rational application of fertilizer.

Keywords: Genetic algorithm, RBF Neural Network, Spatial forecast, Error analysis

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ABOUT THE AUTHORS

Jianbo Xu
College of Infoematics, South China Agricultural University, Guangzhou, 510642, China

Quanyuan Tan
Hunan City University, Yiyang, 413000, China

Lisheng Song
College of Infoematics, South China Agricultural University, Guangzhou, 510642, China

Kai Hao
College of Infoematics, South China Agricultural University, Guangzhou, 510642, China

Ke Xiao
College of Infoematics, South China Agricultural University, Guangzhou, 510642, China


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