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

Sunwen Du,Zengbing Deng,Jin Zhang,Jingtao Li

doi:10.5755/j01.eee.20.4.4238

Abstract

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

Highlights

The geological disasters such as mining subsidence and mine slope landslide are widespread in the deep loose layers of mining regions in Eastern and Northern China, leading to huge economic loss and catastrophe in the mining production activities [1,] [2]
The prediction error of the genetic algorithm optimized BP neural network (GA-back propagation neural network (BPNN)) is much smaller than that of the radial basis function neural network (RBFNN) and BPNN. This comparison indicates that taking the advantage of the GA optimization, the BPNN could be trained well with high genelization ability and the forecasting performance is superior to the unoptimized neural networks
It seems that the trained BPNN is better than the GA-BPNN; it is evident in Fig. 9 that the prediction performance of BPNN is lower than the GA-BPNN

Summary

INTRODUCTION

The geological disasters such as mining subsidence and mine slope landslide are widespread in the deep loose layers of mining regions in Eastern and Northern China, leading to huge economic loss and catastrophe in the mining production activities [1,] [2]. The security level of the mining slope is evaluated through forecasting and monitoring the deformation of the mine slope [3]. This has been achieved by utilizing all kinds of related external influencing factors such. Many researches have been done using the geological factors for the purpose of mining slope deformation monitoring while very limited work has been done using meteorological factors. The novelty of the work is that for the first time, the GA optimized BPNN has been introduced to establish the nonlinear mapping relationship between slope deformation and its meteorological influencing factors. Experimental tests have been carried out to evaluate and validate the performance of the proposed method for mine slope deformation forecasting

Influence Mechanism of the Meteorological Factors

The Proposed Forecasting Approach

EXPERIMENTAL SETUP

APPLICATION OF PROPOSED FORECASTING METHOD

CONCLUSIONS