## 5 Conclusions  
The projected intensification of monsoon rainfall over Southeast Asia and continuous growth of the city is expected to increase landslide incidents in Kuala Lumpur, Malaysia. This calls for reliable demarcation of areas within the city that are susceptible to landslides to support disaster risk management. Kuala Lumpur is underlain by heterogeneous lithology and north-south trending geological structures and geomorphological features, which limits the effectiveness of conventional spatial partitioning techniques for validation in landslide susceptibility modelling. This limitation was overcome by employing a new spatial partitioning technique using a series of two alternating east-west linear zones, where the first zone served as the training dataset and the second zone was the test dataset, and vice versa. The portioning resulted in an inclusive distribution of landslide conditioning factor classes; the difference in the area of factor classes between the two zones is mainly less than 10%. The ROC curves show that the susceptibility models have good compatibility with the selected landslide conditioning factors and have high predictive accuracy. Model A with the highest AUC values (SRC = 0.92, PRC = 0.90) was submitted to DBKL for  
Figure 6: Population density map of Kuala Lumpur at district level based on 2020 census data.  
land use planning and development control. Retrospective revalidation reaffirmed the high predictive ability (AUC of PRC = 0.93) of the landslide susceptibility model. The findings indicates that the product is reliable for decision-making purposes, supporting local level hazard and risk mitigation efforts and better ground prediction in future city development The authors suggest future work to be done using other established methods where comparison of the findings can be done, as data acquisition of landslide occurrences and precipitation data within the study area improves.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13020768/s1](https://www.mdpi.com/article/10.3390/app13020768/s1)]([https://www.mdpi.com/article/10.3390/app13020768/s1](https://www.mdpi.com/article/10.3390/app13020768/s1)), Table S1: (a) The weights of the 14 landslide conditioning factors. Only the first seven factors were used in model 1. (b) The weights for model A where all the 377 landslide points in zone A were used as the training dataset. (c) The weights for model B where all the 274 landslide points in zone B were used as the training dataset. Table S2: Comparison of the area extent of the landslide conditioning factor classes in zone A and zone B.  
Funding acquisition, J.J.P.; resources, J.J.P.; conceptualisation, T.F.N., E.A., V.J.B. and F.A.; data collection (2014 landslide inventory), F.A.; field investigation, E.A. and T.F.N.; data analysis, E.A.; writing, E.A., T.F.N. and J.J.P.; editing, T.F.N., J.J.P. and V.J.B. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Newton-Ungku Omar Fund (Application ID: 59348-455144, UM Project No. IF002-2017, UKM Project No. XX-2017-002) for supporting this research.  
Not applicable.  
The datasets generated during and analysed are available from the corresponding author on reasonable request.  
The authors would like to thank the Editor for the opportunity to be part of this special issue. The assistance of project members from multiple institutions is gratefully acknowledged.  
The authors declare no conflict of interests.