Affandi, Elanni and Ng, Tham Fatt and Pereira, Joy J. and Ahmad, Ferdaus and Banks, Vanessa J. (2023) Revalidation technique on landslide susceptibility modelling: An approach to local level disaster risk management in Kuala Lumpur, Malaysia. Applied Sciences-Basel, 13 (2). ISSN 2076-3417, DOI https://doi.org/10.3390/app13020768.
Full text not available from this repository.Abstract
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: | Article |
|---|---|
| Funders: | Newton-Ungku Omar Fund, IF002–2017, Universiti Kebangsaan Malaysia, XX-2017-002 |
| Uncontrolled Keywords: | Landslide susceptibility; Validation; Predictive capability; Disaster risk; Tropical climate; Malaysia |
| Subjects: | Q Science > QE Geology |
| Divisions: | Faculty of Science > Department of Geology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 29 Nov 2023 02:12 |
| Last Modified: | 29 Nov 2023 02:12 |
| URI: | http://eprints.um.edu.my/id/eprint/38914 |
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