Study on deformation prediction of landslide based on genetic algorithm and improved BP neural network

摘要

Purpose - The purpose of this paper is to improve back propagation neural network (BPNN) modeling in order to promote the forecast calculation precision of landslide deformation. Design/methodology/approach - The genetic algorithm is adopted to optimize the architectural parameter of BPNN so as to avoided errors occurrence while using the trial-and-error method. Furthermore, the Sigmoid function is improved and revised to expand the output range of change-over function from unipolar (only positive) to ambipolar (may be positive or negative), then the convergence time is reduced and the neural network can express more artificial intelligence. Findings - The modeling can effectively reduce the probability to get into the local minima while employing neural networks to forecast the landslide deformation. It significantly promotes the forecast precision. Research limitations/implications - The improved BPNN modeling, which is very good in learning and processing information, can work out the complex non-linear relation by learning model and using the present data or reciprocity of surroundings. Practical implications - The revised BPNN modeling in this paper can be used to predict and calculate landslide deformation. Originality/value - The paper demonstrates that the modeling can meet the demand of calculation precision.