Prediction of mine water inflow based on Chaos-GeneralizedRegression Neural Network

A new prediction model (Chaos-GRNN model) coupled with chaos theory and generalized regression neural network was established to solve the problems of less consideration of relevant influencing factors,low model prediction accuracy and poor applicability in mine water inflow prediction research. The mechanism of chaotic phenomenon in mine hydrological system was theoretically analyzed; the embedding dimension,time delay and maximum Lyapunov exponent of the water inflow time series were calculated by chaos theory,so as to determine the number of neurons in the input layer of GRNN. The number and the value of input layer neurons,and the prediction duration of the model were determined by the m dimensions’sequence.The smoothing factor of GRNN was obtained by cross-validation method. Finally,the Chaos-GRNN model was established. As an example,the water inflow from January 2014 to December 2015 of Pingdingshan Coal Mine No.12 was predicted. The results show that： the cyclic iteration of mine hydrological system evolution process was the fundamental cause of chaos,and its representation features are irreversibility,nonstationarity and diversity of evolution results; the water inflow time series of Pingdingshan Coal Mine No.12 has chaotic characteristics,its embedding dimension M=7,that is,the influencing factors of water inflow are 7,and the number of neurons in the input layer of GRNN is 7; the time delay τ=13 months,which determines the value of neurons in the input layer of GRNN; the maximum Lyapunov index is 0.053 0,and the prediction time of GRNN is 19 months; the prediction accuracy of the Chaos-GRNN model was 94.98%. Chaos-GRNN model used chaos theory to quantify the number and the value of input layer neurons,and prediction duration of the model. This model fully considered the influencing factors of mine water inflow,improved both prediction accuracy and the applicability of model.