Lin Y.-TYen H.-YChang N.-HLin H.-MHan J.-YYang K.-HChen C.-SZheng H.-KHsu J.-Y.JEN-YU HAN2022-03-222022-03-22202110155856https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109012634&doi=10.6652%2fJoCICHE.202104_33%282%29.0002&partnerID=40&md5=78e22bc536ae2f7c1109328a5a8dbfa1https://scholars.lib.ntu.edu.tw/handle/123456789/598474Taiwan's mountainous areas feature steep terrain and broken geological environment. With the invasion of typhoons or heavy rain events, slope disasters such as landslide and debris flow can occur frequently. As a result, how to effectively apply multiple spatial data to predict landslide has become a forwardlooking topic for current disaster management. The development of remote sensing detection technology, such as satellite spectral images and interferometric synthetic aperture radar (InSAR) collected in a fixed period is able to help obtain information on earth surface properties and geometric changes. Through the spatial analysis of the slop units, this research quantifies a total of 14 spatiotemporal factors. The correlation analysis is employed to detect factors that significantly cause landslides, and then artificial intelligence machine learning is used to construct a landslide prediction model. Finally, the confusion matrix verifies the prediction results and evaluates the quality. The research shows that the landslide prediction is better than 80% correct and points out the spatiotemporal factors that affect the slope unit collapse in the survey area. This result also suggests clearer conservation sites and potential assessment for land conservation. The overall study will be effectively used as the application for project site selection, slope protection, and disaster prevention management. ? 2021, Chinese Institute of Civil and Hydraulic Engineering. All right reserved.Correlation analysisLandslide predictionMachine learningThe displacement gradient of InSARConservationDisaster preventionDisastersFactor analysisForecastingLandslidesPredictive analyticsQuality controlRemote sensingSite selectionSlope protectionSpectroscopyDetection technologyDisaster managementGeological environmentInterferometric synthetic aperture radarsMachine learning techniquesPrevention managementsSynthetic aperture radar[SDGs]SDG15journal article10.6652/JoCICHE.202104_33(2).00022-s2.0-85109012634