## 1 Introduction
The result has shown that the surface subsidence time series presented nonlinear subsidence with pronounced seasonal variations, which determined the spatial-temporal characteristics of wide-area surface subsidence and the relationship between surface subsidence and influencing factors. In addition to detecting the ground deformation, it is of vital importance to explore the effects from related elements on deformation to predict future potential change. However, more research is needed on the direction of ground deformation prediction. [PERSON] et al. [32] proposed a theory of the deep mining subsidence dynamic prediction method, which combined the D-InSAR and probability-integral method. This method can provide the subsidence area with a rapid and dependable referenceto judge the damage to environmental resources. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning such as back-propagation (BP) can automatically analyze and extract information from data, and the combination of remote sensing research is very promising [33; 34]. [PERSON] [35] applied machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes, to forecast earthquakes through the laboratory quake cycle. To date, the machine learning method has not been widely used in the application related with InSAR technology. [PERSON] et al. [36] proposed an intelligent method to achieve fully automated extraction of the fringe skeletons in Electronic Speckle Pattern Interferometry (ESPI) based on a U-Net convolutional neural network, which was used to reach the goal of intelligent fringe processing in optical interferometry. [PERSON] et al. [37] first adopted machine learning approaches to detect volcanic deformation in large data sets. The results demonstrated that machine learning has obtained the potential ability for developing alert systems based on satellite imagery to detect large, rapid deformation signals in wrapped interferograms. The Erhai region is a famous scenic spot in Yunnan province, China, and the economic development is mainly based on tourism, agriculture, and forestry. However, due to the expansion of the construction area, the impact of human economic activities on the environment is increasing, which also has presented great threats to the ground deformation. In recent decades, the Erhai region has witnessed rapid socioeconomic development, and thus the acceleration of urbanization, where ground deformation has also increasingly presented damages [38; 39]. As an important National Nature Reserve, detecting the ground deformation of Erhai and analyzing its causal factors are of vital importance; however, no comprehensive studies on ground deformation in Erhai have been carried out. In this study, the objectives are to: (1) generate and validate spatial-temporal ground deformation information using Sentinel-1 images from years 2015-2018 with the SBAS-InSAR method in the whole Erhai region; (2) predict the cumulative ground deformation using BP with influencing factors such as building area, water level, cumulative precipitation, and cumulative temperature; and (3) provide adequate countermeasures and suggestions to adverse the potential danger from the growing cumulative ground deformation. The paper is organized as follows: Section 2 described the study area and data sets. Section 3 introduced the method of processing. Section 4 presented the results and validations of ground deformation from 2015 to 2018, and the results of BP predictions. Section 5 discussed the causes of ground deformation, future ground deformation prediction, and suggestions. Section 6 presented the conclusions.