# Integration of Geoscience Information for Disaster Resilience in Kuala Lumpur, Malaysia
## 2 Materials and Methods
### Assessment of Geoscience Information
The weighting value for each class factor maps represents the correlation factor with the landslide population determined by calculation of the natural logarithm of landslide density of each class within each factor divided by the overall landslide density as expressed in Equation (1) [56]. The reclassified factor maps using the respective statistical weightage were then overlain in ArcGIS to produce the final landslide susceptibility map using Equation (2). The results were classified into 5 classes represented by very low, low, moderate, high, and very high susceptibility-based percentiles of landslide occurrences [57]. The threshold criteria for each landslide susceptibility class were obtained by extracting the landslide index value at each landslide point. A graph of cumulative percentage of the landslide value was plotted against the susceptibility value. The threshold value was extracted from the 50 th, 25 th, 12.5 th and 6.25 th percentile of the susceptibility value. The spatial cross-validation and retrospective methods were used to validate the landslide susceptibility model [18]. \[W_{i}=\ln\left(\frac{\textit{Densclass}}{\textit{Densmap}}\right)\]  
\[W_{i}=\ln\left(\frac{\frac{\textit{Npix}(S_{i})}{\textit{Npix}(N_{i})}}{\frac {\textit{Npix}(S_{i})}{\sum\textit{Npix}(N_{i})}}\right) \tag{1}\]  
where \(W_{i}\) = weight given to a certain parameter class, _Densclass_ = landslide density within the parameter class, _Densmap_ = landslide density within the entire map, \(Npix(S_{i})\) = number of pixels with landslide occurrence in a certain parameter class, and \(Npix(N_{i})\) = total number of pixels in a certain parameter class. \[\text{LSI}=W_{i}\text{SI}+W_{i}\text{Sm}+W_{i}\text{DI}+W_{i}\text{Dr}+W_{i} \text{El}+W_{i}\text{Ro}+W_{i}\text{Tpi} \tag{2}\]  
where LSI = landslide susceptibility index, \(W_{i}\text{SI}\) = weight of slope gradient, \(W_{i}\text{Sm}\) = weight of surface material, \(W_{i}\text{DI}\) = weight of distance to lineament, \(W_{i}\text{Dr}\) = weight of distance to road, \(W_{i}\text{El}\) = weight of elevation, \(W_{i}\text{Ro}\) = weight of roughness and \(W_{i}\text{Tpi}\) = weight of topographic position index. The flood susceptibility model was developed using JFlow(r), which is a hydraulic modelling software developed by JBA Risk Management (JBA), United Kingdom. Benchmarking exercises conducted in 2012 revealed a high-performance model, producing fast and accurate representations of flow routing across flood plains. The 5 m grid model created using JFlow(r) and GIS was derived by using 1 m DTM to define the appropriate rainfall and river flow input with various flood return periods in the study area of Kuala Lumpur [58]. The model showcased a defended scenario where it considered the Stormwater Management and Road Tunnel (SMART) tunnel system and other flood mitigation projects emplaced within the city showing 20-, 50-, 100, and 200-year return periods. Flood susceptible areas were demarcated using two-dimensional flow paths to capture both river and pluvial floods [32; 58]. Two types of validation were conducted. Comparison with historical flood events was used to validate the flood extent, and the discharge rates through culverts were used to validate the defended flood scenario [50].