###### Abstract
DInSAR calculates the phase variation of two SAR images acquired in the same region at different times. Long-term InSAR observations are calculated as the deformation-induced phase shift through the backscattered microwave signal between several coherent acquisitions. The landslide behavior of time-series information, which depends on the millimetric measurement accuracy and the metric spatial resolution, is obtained under most atmospheric conditions [11]. Landside prediction methods can be classified into three types: image analysis, mathematical evaluation models, and machine learning methods [12]. Image analysis uses geographic information systems, which can collect, store, manage, and analyze geographical data. The risk of landslides can be predicted by analyzing disaster data, such as history of landslides and land. The probability of landslides varies because it is based on the number of data layers used for analysis. Mathematical evaluation models use a single evaluation equation that is combined with the physical concepts of mechanics and hydrographic data, such as rainfall, runoff, and infiltration data, for landslide susceptibility assessment [13]. The use of such models is easy for simulation and fits a wide range of environments. However, mathematical evaluation models require detailed data of the geotechnical engineering and geological aspects of slope failure at sites [14], which makes these models costly and impractical for large-scale areas. In recent years, machine learning and data mining techniques, such as support vector machine, artificial neural network, and decision tree (DT) models, have been applied for landslide susceptibility modeling [15]. These methods incorporate different factors that might cause landslides to evaluate the probability of landslide occurrence. Machine learning algorithms enrich the quality and accuracy of generated susceptibility maps. Researchers use and compare various machine learning models on the basis of different data [16; 17; 18; 19], integrate different machine learning models to improve accuracy [20; 21; 22; 23],or develop new algorithms that are based on traditional machine learning models to strengthen landslide prediction results [24; 25; 26]. These techniques perform better than do classical methods. Most machine learning techniques achieve overall success rates of 75% to 95% [27]. Although many applications have demonstrated the feasibility of data-driven models for capturing nonlinear relationships and modeling the dynamic processes of landslides on the basis of historical model data, certain limitations remain [28]. As shown, landslide behavior involves temporal dependencies. However, common machine learning models ignore this intrinsic temporal dependency, which involves the effect of preceding actions on present actions in the model [29; 30]. The solution proposed by this study is to combine spatial-temporal data, including InSAR observables, as a landslide susceptibility factor with other traditional geological and land cover factors into a model that can improve the prediction accuracy of potential landslides. To our knowledge, integrating InSAR observables and multiple geological factors for landslide susceptibility analysis is an effective and pioneered contribution for landslide potential prediction research.