Application of grey feed forward back propagation-neural network model based on wavelet denoising to predict the residual settlement of goafs.

ABSTRACT: To study the residual settlement of goaf's law and prediction model, we investigated the Mentougou mining area in Beijing as an example. Using MATLAB software, the wavelet threshold denoising method was used to optimize measured data, and the grey model (GM) and feed forward back propagation neural network model (FFBPNN) were combined. A grey feed forward back propagation neural network (GM-FFBPNN) model based on wavelet denoising was proposed, the prediction accuracy of different models was calculated, and the prediction results were compared with original data. The results showed that the prediction accuracy of the GM-FFBPNN was higher than that of the individual GM and FFBPNN models. The mean absolute percentage error (MAPE) of the combined model was 7.39%, the root mean square error (R

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10159359 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature