A New Approach of Geological Disasters Forecasting using Meteorological Factors based on Genetic Algorithm Optimized BP Neural Network

Sunwen Du, Jin Zhang, Zengbing Deng, Jingtao Li


The monitoring and forecasting of the mining slope deformation are of great significance to prevent potential geological disasters in mining regions and the geological factors have been widely used for the purpose of mining slope deformation monitoring. However, literature review shows that very little work has been done in prediction of mining slope deformation using meteorological factors. To address this issue, a new method is proposed using the meteorological factors to forecast the mining slope deformation. Herein, the meteorological factors include the temperature, atmospheric pressure, cumulative rainfall, relative humidity and refractive index of the mining slope. A genetic algorithm optimized BP neural network (GA-BPNN) was employed to fuse the meteorological factors to establish the prediction model for the mining slope deformation. The experiments have been implemented to evaluate the new approach and a comparison between the GA-BPNN, BPNN and radical basis function neural network (RBF) prediction models has been carried out. The analysis results show that the proposed method can provide precise prediction of the mining slope deformation and its performance is superior to its rivals.

DOI: http://dx.doi.org/10.5755/j01.eee.20.4.4238


Geologic measurements; meteorological factors; forecasting; artificial neural networks

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