Research on Groundwater Level Prediction Method in Karst Areas Based on Improved Attention Mechanism Fusion Time Convolutional Network

Abstract

A new prediction method based on improved attention mechanism and time convolutional network fusion is proposed for the prediction of groundwater level in karst areas. Within the overall framework of the prediction method, historical water level, flow rate, and rainfall were selected as input data. The input data is processed by the time attention module and the feature attention module respectively to form a weight matrix corresponding to the data sequence, and then trained and learned using a time convolutional network to complete prediction. Experimental results show that the proposed method is significantly better than LSTM method, RNN method and CNN method in terms of mean absolute error and root-mean-square deviation. The predicted change curves at the three measurement points also form a good agreement with the actual groundwater level change curve.