The prediction method of soil moisture content based on multiple regression and RBF neural network

摘要

In the application of the field repair in the countryside, determination of the moisture content is very important. Compared with the traditional methods, ground penetrating radar (GPR) can measure soil moisture content in a wide region at the same time. This paper presents a prediction method about the moisture content based on the multiple regression and the radial basis function (RBF) neural network. Firstly, we measured the moisture content by experiments and compared the information in GPR data. Secondly, we use multiple regression analysis to get the active components affecting the moisture content in GPR data. Through utilizing the active components and the soil moisture content, we can train RBF neural network. Finally, optimize and record the network. In the practical application, aiming at a particular frequency of GPR, through multiple regression, we can predict the soil moisture content better than the RBF neural network only. This method not only can meet the needs of determination of the soil moisture content, but also can make a necessary help for the field repair in the countryside.