Prediction of open air water inflow based on improved multivariate time series model
Changes in the amount of water surging from open pits affect the stability of slopes,the rate of decline of the project and the service life of the equipment.During the flood season of the mine,the sudden increase in the amount of water surging poses a huge safety hazard to the mine.Therefore,in order to do a good job in the surge of water safety precautions,the accurate prediction of the flood season surge of water has become a major problem of mine safety production.To address this problem,this paper proposes a method based on the Sparrow Initialization Dung Beetle Optimizer(SIDBO)algorithm to optimize the Variational Mode Decomposition(VMD)-Bi-directional Long Short-Term Memory(BiLSTM)time series model to predict the water inflow of open pit.BiLSTM temporal modeling method for predicting water influx in open pits.For the problem of difficult to determine VMD parameters,an improved dung beetle optimization algorithm is used to find the optimal VMD core parameter combinations.The SIDBO algorithm first optimizes its exploration phase based on the t-distributed difference strategy,enhances its global optimal solution searching capability using the optimal solution and the second solution median searching strategy,and finally optimizes its development phase using the sparrow optimization algorithm.The BiLSTM is optimized by SIDBO,and SIDBO optimizes its three parameters:the optimal number of hidden units,the optimal training period,and the optimal initial learning rate.The optimized VMD decomposition components and the mine rainfall data are brought into the super-parameter-optimized BiLSTM to make predictions,and finally the predictions are accumulated and summed up.The results show that the SIDBO-VMD-SIDBO-BiLSTM model has higher prediction accuracy compared with other models such as VMD-SIDBO-LSTM.The four indexes of Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and R2 are 5.96,4.96,0.41％,and 0.98,respectively.Comparing this model with the traditional geological method-water equalization method in the actual engineering examples,this time series model can improve the accuracy of water influx prediction for the flood season of the pit by 3.8％,providing a new technical method and idea for the prediction of flood season of the open pit mine and supporting safety production.
safety engineeringalgorithm optimizationtime series predictionVariational Modal Decomposition(VMD)deep learning
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