Revalidation Technique on Landslide Susceptibility Modelling: An Approach to Local Level Disaster Risk Management in Kuala Lumpur, Malaysia

Affandi, Elanni; Ng, Tham Fatt; Pereira, Joy J.; Ahmad, Ferdaus; Banks, Vanessa J.. 2023 Revalidation Technique on Landslide Susceptibility Modelling: An Approach to Local Level Disaster Risk Management in Kuala Lumpur, Malaysia. Applied Sciences, 13 (2), 768. 10.3390/app13020768

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Official URL: http://dx.doi.org/10.3390/app13020768

Abstract/Summary

Landslide susceptibility modelling in tropical climates is hindered by incomplete inventory due to rapid development and natural processes that obliterate field evidence, making validation a challenge. Susceptibility modelling was conducted in Kuala Lumpur, Malaysia using a new spatial partitioning technique for cross-validation. This involved 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 results show that the susceptibility models have good compatibility with the selected landslide conditioning factors and high predictive accuracy. The model with the highest area under curve (AUC) values (SRC = 0.92, PRC = 0.90) was submitted to the City Council of Kuala Lumpur for land use planning and development control. Rainfall-induced landslides are prominent within the study area, especially during the monsoon period. An extreme rainfall event in December 2021 that triggered 122 landslides provided an opportunity to conduct retrospective validation of the model; the high predictive capability (AUC of PRC = 0.93) was reaffirmed. The findings proved that retrospective validation is vital for landslide susceptibility modelling, especially where the inventory is not of the best quality. This is to encourage wider usage and acceptance among end users, especially decision-makers in cities, to support disaster risk management in a changing climate.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3390/app13020768
ISSN:	2076-3417
Date made live:	21 Feb 2023 14:40 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/534059

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.3390/app13020768

ISSN:

2076-3417

Date made live:

21 Feb 2023 14:40 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/534059