## 2 Materials and Methods
### Landslide Conditioning Factors  
A total of fourteen intrinsic landslide conditioning factors were prepared from the geological maps, DTM, topographic maps, and satellite imageries. All the geoprocessing was done using the software ArcGIS 10.5. Bedrock geology and surface geology were extracted from the geological data. Geomorphological factors such as slope gradient, slope aspect, and slope curvature were derived directly from the DTM. The standard deviation of elevation calculated from a 10 m by 10 m moving window was used to derive the surface roughness [40]. The topographical position index (TPI) [41; 42] was calculated as the difference between the elevation at a point and the mean elevation within a 250 m radius circular moving window. The topographic wetness index (TWI) is a contributing factor that affects the topography and used to quantify the topographic control on hydrological processes, expressed by Equation (1), whereas the stream power index (SPI) measures the erosive power of water flow as defined by Equation (2). The value \(\alpha\) is calculated by multiplying the flow accumulation with the cell area. The distance to stream, distance to road and distance to lineament maps were created by making multiple ring buffer around the streams, roads and lineament, respectively. Landsat 8 imagery was used to produce the normalised difference vegetation index (NDVI) map. The value can be calculated using Equation (3). \[\text{TWI}=\ln(\frac{\alpha}{\tan\beta}) \tag{1}\]  
Figure 1: Geological map of the Kuala Lumpur area. The city of Kuala Lumpur is located within the Selangor state, in central-west Peninsula Malaysia. \[\text{SPI}=\ln(\alpha.\tan\beta) \tag{2}\]  
where \(\alpha\) is the local upslope contributing area, and \(\beta\) is the slope [43]. \[\text{NDVI}=\frac{NIR-Red}{NIR+Red} \tag{3}\]  
where \(NIR\) is the near-infrared band, and \(Red\) is the red band. An expert consultation process was carried out to select the best landslide conditioning factors for the area [9]. The conditioning factors went through an iterative selection and elimination procedure by experts with experience working on landslides in tropical terrain. The evaluation was based on redundancy and relevance of the factors to the local geological and terrain conditions, as well as the initial results of bivariate statistical analysis. Only seven of the original conditioning factors were selected as shown in Table 1. They are surface geology, slope gradient, elevation, distance to lineament, distance to road, surface roughness, and TPI (Figures 2 and 3). Most of the ground is covered by surface geology and shows a strong influence towards landslide initiation compared to bedrock geology, caused by the deep weathering profile which generates thick residual soil over most area [44]. The residual soil of Kenny Hill formation holds the highest weightage of 1.00 with 68.7% of total landslide, due to the interlayering of metasedimentary rocks of variable strength and properties while the granite represents 127 (20%) landslides with a weightage value of \(-0.18\). Slope gradient is usually regarded as the primary causative factor for the onset of slope failure associated to shear strength of the material on slope [45]. The analysis of the parameter indicates that the landslide density and weightage values increase with increasing slope gradient. Elevation of the slope is not a conditioning factor by itself but affects the overall surface of the terrain and topographic features which controls the vegetation distribution [31]. The highest weightage is represented by the 100-150 m class with a value of 0.55 and has 10% of the total landslides while 50-100 m class represents 68.8% of the total landslides with a lower weightage of 0.49 due to the larger area. The correlation between distance to lineament parameter and landslides is explained by weakened earth material by deformation along faults.