Research on water inflow prediction of working face based on CEEMDAN and improved hybrid time series model
In order to improve the prediction accuracy of water inflow in coal mining face,a large number of observation data of water inflow in coal mining face were collected for collation,statistics and analysis.Taking into account the stability,perio-dicity and seasonal characteristics of water inflow,a prediction method of water inflow in working face based on the data-driv-en adaptive noise-complete set empirical mode decomposition algorithm(CEEMDAN)and the hybrid time series model was proposed.In this method,the water inflow data was processed by using CEEMDAN,and a hybrid time series model formed by the parallel concatenation of long short-term memory network(LSTM)optimized by sparrow search algorithm(SSA)and au-toregressive integrated moving average model(ARIMA)was constructed to predict the water inflow of working face.The re-sults show that the difference between the prediction results of the hybrid model and the real data is smaller,and it is more suitable for the prediction of water inflow in working face.The average absolute error of the model prediction results is reduced to 6.36 m3/h,the root mean square error is reduced to 10.6 m3/h,and the model fit coefficients are 0.95,which not only overcomes the interference of other related influencing factors,but also improves the prediction accuracy and speeds up the prediction speed.The research results can provide a reference for the prediction and prevention of water inflow in mine work-ing faces.
water inflow predictiontime series predictionhybrid modelempirical mode decomposition(EMD)sparrow search algorithm(SSA)
NETL
NSTL
万方数据
