Combined genetic K-means and radial basis function neural network technique for classifying and predicting soil moisture

Abstract

A combined technique of genetic k-means and radial basis function neural network (RBFNN) is used in this study to process remote sensing data and classify soil basing on its moisture content. Radial basis function neural network is used for its advantages of rapid training, generality and simplicity over feed-forward backpropagation neural network. The genetic k-means clustering is used to choose the initial radial basis centers and widths for the RBFNN. An attempt is also made to study the performance of the RBFNN with the centers and widths chosen using the classical k-means clustering. The results showed that genetic algorithms give global optimal centers and widths for the RBFNN. The results also indicated that this hybrid technique can be used in soil moisture classification and prediction.