# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 4 Results
### Landslide Susceptibility Mapping  
The landslide susceptibility of the entire study area was evaluated using the aforementioned models. The probability of landslide occurrence at each spatial location was predicted pixel by pixel based on spatial indices and calculated the likelihood of it belonging to the landslide class, followed by spatial visualization. In ArcGIS 10.8 software, the Jenks statistical method was employed to classify the landslide susceptibility of the entire study area into five levels: very low, low, middle, high, and very high.  
In the 2000-2004 time range (Figures 9a and 10a), both the RF and SVM models predicted two very high areas in the central and western regions of the study area. Combined with Figure 2a, it can be observed that these areas have a relatively dense distribution of  
In the 2005-2009 time range (Figures 9b and 10b), a severe landslide disaster occurred in Lantau Island in 2008, with more than 3000 recorded landslide points. Therefore, both the RF and SVM models predicted a very high landslide susceptibility in the Lantau Island area. In other regions, the prediction results of the RF and SVM models were relatively similar in spatial distribution. Landslide disasters were mainly concentrated on Lantau Island during this time period, while the probability of landslide occurrence in other areas of Hong Kong was moderately low.  
In the 2010-2014 time range (Figures 9c and 10c), the prediction results of the RF and SVM models again showed similar patterns in the northern area around the High Island Reservoir in the northeastern part of the study area. This region has a relatively high number of historical landslide points, which is consistent with the results of landslide susceptibility. In the northern area of the Plover Cove Reservoir, there are also a large number of historical landslide points, and the prediction results of both models are also similar to the spatial distribution pattern of historical landslide points.  
In the 2015-2019 time range (Figures 9d and 10d), historical landslide points were clustered in two clusters. The first cluster is located in the northeastern part of the study area, similar to the distribution of landslides in the 2010-2014 time range. The other cluster is located in the central-northern part of the study area, where the elevation is significantly higher than the surrounding areas. However, the prediction results of both models were not very accurate. The RF model overlooked the historical landslide points in the central-northern part of the study area, while the SVM model predicted most of the study area as having moderate to high susceptibility, which is unreasonable. It is conceivable that during the selection phase of non-landslide samples, the commonly used random sampling method was applied. This may have resulted in the inclusion of samples from areas with high susceptibility, potentially influencing the model's accuracy. While the model's performance is not without limitations, the integration with geospatial visualization data revealed a strong correlation between the predicted results and established geological knowledge. This correspondence indirectly supports the reliability and applicability of the research findings.