## 4 Discussion  
The sole official landslide inventory established in 2014 contained only 650 landslide points that could be verified despite the numerous slope failures have been reported in Kuala Lumpur for decades, especially after a severe rainfall. This is primarily because much of the evidence in the field was obliterated. In comparison, after one unusually heavy rainfall incident, 122 events were recorded by the research team. This indicates that the number of historical landslides is most likely higher and has gone unrecorded. Furthermore, information in the original 2014 landslide inventory was limited to event locations, while more complete information was obtained from the fresh landslide scars in 2021; indicating the need for urgency in field data collection. Though the 2014 inventory was manually improved to correctly position and interpret the type and size of landslides using DTM and orthorectified aerial photographs; much time and expertise on local conditions was required. An automated approach has been advocated for the identification of landslides using high resolution DEM [7]. However, the accuracy of this method in cities is yet to be reported. In order to obtain complete and better-quality datasets, landslide inventories in tropical cities need to be routinely updated after heavy rainfalls. Landslide inventories with information on landslide masses reportedly produced more accurate susceptibility maps compared to the use of landslide mass centre point [7]. However, it has also been reported that the scarp has higher predictive accuracy than the mass centre as it closely represents the pre-failure conditions [56]. The 2014 inventory utilised the position of the landslide scarp centre as it was a more prominent feature that was more readily identified. In most cases, the body and toe of the landslide is not clear due to erosion and vegetation. In this study, the inventory sourced from JMG was not of the best  
Figure 5: (**a**) ROC curves of spatial model evaluation and validation with associated AUC values for model 1, model A, and model B. (**b**) ROC curves of retrospective validation with associated AUC values for the same models. quality with respect to data on the type and extent of the landslides. Notwithstanding, a consistent sampling technique using information on landslide scarp resulted in sufficiently accurate susceptibility models. The landslide conditioning factors used for modelling were restricted to 7, although a total of 14 factors were initially produced. This smaller selection was considered appropriate through expert consultation and resulted in a susceptibility model that had high prediction accuracy. In this investigation, the high resolution (1 m) Light Detection and Ranging (LiDAR) derived DTM was resampled to 5 m, to reduce the noise, artifacts, and processing time. This was also considered sufficient as the topographic data (DTM) was of relatively high resolution. It has been demonstrated that finer resolution of topographic data leads to more accurate and precise susceptibility models. This study supports recent findings that as long as suitable input data and techniques are selected based on the data quality and purpose, most landslide susceptibility models result in sound overall prediction accuracy regardless of the approach [7]. Retrospective validation conducted in this study indicates that the spatial partitioning technique along alternating linear zones trending east-west that was used to cross validate the susceptibility models is able to resolve the issue of heterogeneity caused by north-south trending geological and geomorphological features. All the landslide conditioning factor classes were represented in both zones A and B (Supplementary, Table S1). The difference in the area for 70% of the factor classes between the two zones is less than 10% (Supplementary Table S2). It appears that spatial partitioning techniques that are context specific, taking into account local geological and geomorphological features, result in high confidence susceptibility maps.