Abstract: In order to solve the problem of difficult extraction of spatial features of monitoring points in the time series prediction of surface collapse areas in the mining zone, a CNN-Attention-LSTM combined neural network model that can extract key spatial features of monitoring points is proposed. Firstly, the number of neighbouring monitoring points as feature input is increased, and the spatial features of the multidimensional time series composed of multiple monitoring points are extracted using convolutional neural network (CNN). Secondly, the extracted multidimensional feature time series are input into the multilayer perceptron (MLP) to calculate the attention weights and make Hadamard product with the feature inputs to achieve the allocation of the attention weights of the feature inputs. After that regression prediction is performed using long short term memory neural network (LSTM). Finally, through the fully connected layer, the predicted values of the target monitoring points are integrated and output. In this paper, we take the surface collapse area in the west second mining area of Longshou mine as an example to give the prediction results of its surface subsidence monitoring data and compare them with the actual collected data. The results show that the combined CNN-Attention-LSTM model with the introduction of the attention mechanism is more accurate than the CNN-LSTM model and the LSTM model respectively, and the addition of effective feature inputs can significantly improve the prediction accuracy of the CNN-Attention-LSTM model.

Key words: time series modeling, surface subsidence prediction, deep learning, attention mechanism, long and short-term memory