Prediction of Land Subsidence Based on Combined CNN-LSTM

As one of geologic factors affecting the sustainable development of regional economy and society, the importance of land subsidence is increasing gradually. And then a high-precision prediction of land subsidence trends is of great significance for the disaster prevention and reduction. However, the common prediction methods have shortcomings with strong constraints, difficult parameter model selection, and weak generalization ability. Therefore, a combination neural network based on CNN and LSTM is proposed. Firstly, the SBAS InSAR analysis method was used to obtain time series monitoring results. Then, the multi-dimensional feature extraction of land subsidence was carried out through spatial interpolation and CNN to extract the features in spatial and temporal. Finally, LSTM was used for the feature learning and prediction of land subsidence. And an area near seaside is chosen for the predication experiment, the cumulative subsidence amount and the subsidence changes in different subsidence interval were reflected from the predicted subsidence results. Three accuracy evaluation were carried out to verify the effectiveness of the combination method. The absolute error of the model prediction was less than 4 mm from regional scale, the mean square error of was less than 2.5 mm from point scale, and the average prediction accuracy was improved about 12% through comparing with common method.