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

Abstract

PurposeThe 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/approachThe 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.FindingsThe 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/implicationsThe 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 implicationsThe revised BPNN modeling in this paper can be used to predict and calculate landslide deformation.Originality/valueThe paper demonstrates that the modeling can meet the demand of calculation precision.