###### Abstract  
Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technology, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a global construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned. Keywords:landslide potential; InSAR; spatial factors; machine learning; slope unite +
Footnote †: journal: Medical  
failure. Therefore, the use of only the single-point method in landslide surveys would limit the cost-effectiveness. In recent years, remote sensing technology has effectively detected large-scale landslide-sensitive areas and generated landslide inventories, which are crucial for predicting landslides before they occur or recur, especially in far or barely accessible areas [5]. In daytime satellite images without shadows and clouds, landslide positions can be identified through noticeable radiometric contrasts between land cover types [6]. Optical sensors cover the electromagnetic spectrum from 390 nm to 1 mm, including the visible and infrared bands. Such devices can measure the visual properties in the spectral characteristics of the land surface, which can then be used to detect and map landslides. Researchers can also combine time-series satellite images with digital elevation models (DEMs) to acquire 3D terrain, which can be used to visually detect and predict potential landslides. However, affected by monsoons, typhoons, and thunderstorms, mountainous areas are usually shrouded in clouds at times; thus, the use of satellite images to monitor landslide disasters could be limited by weather conditions. Compared with optical sensors, synthetic aperture radar (SAR) sensors use a longer wavelength--microwaves; having all-weather and all-day operational capability, SAR sensors can penetrate cloud cover and reduce the limitation imposed by the atmosphere to remotely evaluate the accurate range and severity of landslide disasters in almost real-time [7]. Although some particular meteorological situations, such as thick rain cells, may disturb the backscattering coefficient, SAR remains more powerful than optical sensors for long-term landslide observation [8]. Spaceborne SAR, such as Envisat, ALOS PALSAR, RADARSAT, TerraSAR-X, and Sentinel-1, provide high spatial resolutions and can clearly observe target objects in full-time and in almost all-weather conditions. Numerous applications of SAR data to ground displacement detection have demonstrated their usefulness for landslide characterization and mapping [9]. Differential SAR interferometry (DInSAR) is a commonly used method of ground deformation measurement, and it can efficiently generate or update landslide inventory [10], which is critical information about landslide behavior for landslide susceptibility assessment.