## 1 Introduction  
Landslides triggered by rainfall have resulted in the highest number of fatalities in Asia, and whilst its attribution to climate change is limited by deficient records, landslide occurrence is expected to increase in many regions [1, 2]. Development in steep hills and unstable slopes, in combination with seasonally dry periods followed by excessive rainfall are contributing factors for frequent landslides in Southeast Asia [3]. The number of people in cities and urban settlements that will be exposed to landslides is expected to increase in Southeast Asia due to increased monsoon rainfall as a result of climate change [1, 4]. The expected increase of landslides calls for reliable demarcation of areas susceptible to this hazard for supporting disaster risk management in cities. Quantitative techniques predominate in landslide susceptibility modelling. The techniques could be deterministic (analytically driven), heuristic (knowledge-driven), or data-driven statistics, encompassing bivariate, multivariate, and machine learning approaches [5, 6]. The use of statistical approaches in actual practice is dependent on a reliable inventory that reflects the actual factors that cause a slope to fail [7, 8]. Statistical approachesare reliable for shallow landslides when drawing on inventories that integrate field investigation, high resolution digital elevation models (DEM) and images, combined with expert inputs for more nuanced representation of local conditions [9; 10; 11; 12]. Validation is critical for susceptibility modelling to ensure the quality and reliability of outputs for wider usage among the decision makers [13; 14; 15]. Model validation generally involves two aspects, evaluating compatibility and assessing predictive capability [16; 5; 17]. The area under curve (AUC) is computed from the receiver operating characteristics (ROC) curve to report results. The evaluation of compatibility (i.e., agreement between the outputs and observed data) is from success rate curves (SRC) while the assessment of predictive capability is through prediction rate curves (PRC). Partitioning of the inventory into test and training datasets is the basis for validation [15; 18]. Partitioning of the inventory is handled using three types of techniques. These are random, spatial, and temporal techniques [13; 14; 19; 20]. Temporal validation uses dataset of landslides that occurred at different time periods to assess the predicted model capability. Random partitioning involves separation of the landslide inventory into two subsets, where one subset is arbitrarily removed to assess predictability. Spatial partitioning is a comparison of two mutually exclusive sub-regions of the area under investigation, or to another area with similar setting. This technique has limitations in lithologically heterogeneous areas, where some parameters that influence landslides may be absent in one of the sub-regions, leading to low predictive capability [15; 21]. Retrospective validation has recently been introduced to assess the actual accuracy of prediction from susceptibility modelling [18]. The application of this approach is expected to increase the reliability, transparency, and acceptance of susceptibility modelling. Malaysia has tropical climate with mean daily temperatures of between 26 \({}^{\circ}\)C to 28 \({}^{\circ}\)C and annual average rainfall ranging from 2000 mm to 4000 mm, with two distinct monsoon seasons [22]. The hot and humid conditions have resulted in deep weathered soil profiles that are prone to landslides. Rainfall-induced shallow landslides are common in cities primarily due to land use change and other human activity, and several have been observed to be reactivated failures [23; 24; 25]. In some cases, failures have been linked to inadequate engineering design during construction and poor slope maintenance [23]. Landslide mapping is a challenge due to vegetation growth, active erosion, and rapid development that obliterates evidence of landslide occurrence.