## 4 Discussion  
Throughout the time series, the relevant spatial observation data showed changes in slopes. This study used these environmental observation data to construct the spatial factor indicators on the basis of the slope unit conditions. Significant spatial factors were then determined from the correlation analysis. According to the spatial characteristics of the slope units, the machine learning methods were applied to construct the calculation models, and the landslide potential of the slope units was evaluated. This study was implemented with two experimental cases: Siaolin Village and Putununus River (Kaohsiung, Taiwan). The experiment collected four-year spatial data (topography, locations, geology, driving categories, and landslide locations) from 2007 to 2010. Then, these data were used to construct the 14 spatial factors through indexed analysis. A common timescale (year) was established for the analysis to resolve the differences in timescales of the various spatial factors. The spatial factor datasets from 2007 to 2009 served as the input for the correlation analysis and machine learning, and the 2010 spatial factor data were used to calculate the output of potential evaluation. In the Siaolin Village area, the significant spatial factors were the rock mass strength, aspect, terrain roughness, slope, and dip slopes; the significant spatial factors in the Putunpunas River area were the rock mass strength, aspect, vegetation index, water distance, terrain roughness, and dip slopes. These significant factors in both study areas were all in the geological category, including rock mass strength, terrain roughness, and dip slopes. Obviously, the geological conditions in these areas highly influence the landslide trend. The machine learning algorithms used in this research achieved accuracies of 60-80% in landslide classification. Among them, the random forest method exhibited the best calculation in Siaolin Village, where it yielded a prediction accuracy rate of 82.95%; its prediction accuracy rate in Putunpunas River was 80.50%. The random forest method effectively performed independent training for high-dimensional, multi-feature factors. In addition, the random forest algorithm exhibited strong anti-interference capabilities, such as an imbalance in the number of classifications and missing parts of the feature data, so it could avoid excessive parameter setting and reduce overfitting problems. Moreover, the addition of the InSAR factor increased the accuracy of prediction up to 6%. To further verify the proposed approach, the model established based on the training data from the two study areas was applied to another area in northern Taiwan. In December 2020, a landslide covering a slope area of around 4000 m\({}^{2}\) and 10,000 m\({}^{3}\) in earth volume occurred in this region. By feeding the local spatial factors into the model, the landslide susceptibility of each slope unit was obtained. Figure 7a,b illustrate the validation results from using 13 spatial factors (excluding InSAR data) and 14 spatial factors (including InSAR data), respectively. It shows that a medium (50-75%) landslide potential was obtained for the landslide area if only the geological factors were considered. However, when the InSAR data were included, the model gave a high (>75%) landslide potential for that slope unit. In other words, the InSAR data provided an essential contribution for improving the prediction accuracy, as also revealed in the two study areas previously mentioned. Furthermore, it should be stressed that the model used here was established based on the training data in the two study areas in southern Taiwan, but it can still perform well in this validation case in northern Taiwan. This gives an encouraging indication that the model