Forecasting of underground field intensity based on LS-SVM optimized by genetic algorithm

Abstract

In order to study propagation loss law of electric wave and improve prediction accuracy of field intensity coverage in mine tunnel, least square support vector machine (LS-SVM) method optimized by genetic algorithm was used to forecast underground field intensity in mine tunnel. Firstly, simulated field intensity data was generated by computer software and divided into training set and testing set. Then the LS-SVM machine method was used to study training set, genetic algorithm was used to optimize parameters of LS-SVM, and testing set was used to verify performance of the method. Finally the LS-SVM method optimized by genetic algorithm was used to forecast underground field intensity in mine tunnel. The simulation and experiment results prove that the LS-SVM optimized by genetic algorithm can effectively improve prediction accuracy of field intensity in mine tunnel, and achieve good prediction effect.

Keywords

• mine tunnel
• field intensity forecasting
• least square support vector machine
• genetic algorithm