Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China

Abstract

Ground deformation (GD) has been widely reported as a global issue and is now an ongoing problem that will profoundly endanger the public safety. GD is a complex and dynamic problem with many contributing factors that occur over time. In the literature, there are only a few methods that can effectively monitor GD. Microwave remote sensing data such as interferometric synthetic aperture radar (InSAR) are mostly adopted to assess GD. These data can reveal the surface deforming areas with great precision, mapping GD results at a large scale. In this study, the effects of GD and the influencing factors, such as the building area, the water level, the cumulative precipitation, and the cumulative temperature, are modeled in the Erhai region with small baseline subset interferometric SAR (SBAS-InSAR) data that are applied using machine learning (ML) methods. The ML methods, namely, multiple linear regression (MLR), multilayer perceptron backpropagation (MLP-BP), least squares support vector machine (LSSVM), and particle swarm optimization (PSO)-LSSVM, are used to predict GD, and the results are compared. Particularly, the PSO-LSSVM method has obtained the least root mean square error (RMSE) and mean relative error (MRE) of 11.448 and 0.112, respectively. Therefore, the results have proven that the proposed PSO-LSSVM is very efficient in analyzing GD.