Prediction of InSAR deformation time-series using a long short-term memory neural network

The prediction of land subsidence is a crucial step for early warning of urban infrastructure damage and timely remedy. However, the performance of most mathematical and empirical prediction models is often compromised by their large number of parameters, complex operational processes and sparsely measured values. Currently, the traditional neural network models are popular and effective, but they cannot accurately discover the characteristic changes of time series data. In this paper, a long short-term memory (LSTM) neural network was proposed to predict the land subsidence of time series Interferometric Synthetic Aperture Radar (InSAR). First, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was utilized to monitor the time series land subsidence at Beijing Capital International Airport (BCIA) from 2005 to 2010 based on ENVISAT ASAR images with a descending orbit. The results were compared with the existing results to verify the reliability and then used to analyse the temporal and spatial characteristics of the time series land subsidence of the BCIA. Based on the time series InSAR deformation data, the LSTM neural network was used to establish the prediction model of time series InSAR, and the results were compared with those of the Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). The comparison results showed that the LSTM neural network was more accurate than the MLP and RNN on the point scale (the root mean square error was 4.60 mm and the mean absolute error was 3.18 mm), the correlation coefficients between the prediction results of the LSTM neural network and the real InSAR measurement results in 2007 and 2008 were 0.93 mm and 0.96 mm, respectively, indicating that LSTM neural network had better prediction performance. Eventually, based on the land subsidence data of time series InSAR from 2006 to 2010, the LSTM neural network was applied to predict the BCIA time series land subsidence in 2011. The results predicted that cumulative subsidence in September 2011 would reach a maximum of 350 mm. Therefore, the LSTM neural network is a potentially effective prediction method, which can replace numerical or empirical models in the absence of detailed hydrogeological data. Moreover, its prediction results can be used to assist decision-making, early warning and hazard relief.