Optimizing seismic hazard inputs for co-seismic landslide susceptibility mapping: a probabilistic analysis

Kunal Gupta () and Neelima Satyam ()
Additional contact information
Kunal Gupta: Indian Institute of Technology Indore
Neelima Satyam: Indian Institute of Technology Indore

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 9, No 15, 8459-8481

Abstract: Abstract The significance of seismic hazard maps as inputs in co-seismic landslide susceptibility mapping is well-established. However, a research gap exists as no previous study has compared the effectiveness of various seismic hazard map inputs. The present research conducts a comprehensive comparative study, evaluating probabilistic seismic hazard assessment (PSHA)-based and specific scenario-based PGA maps as inputs for co-seismic landslide susceptibility mapping. In the study, the first step involved generating PSHA-based and scenario-based PGA maps, which served as seismic intensity inputs for the modified Newmark’s model. The modified model incorporates the rock joint shear strength parameters in displacement computations. To address uncertainties associated with the spatial variability of shear strength parameters of rock joints, Latin hypercube sampling along with Monte Carlo simulations were employed, resulting in a set of displacement values. The Latin hypercube sampling method ensured a more efficient and stratified sampling approach, enhancing the representation of uncertainty in the model. The simulations were conducted 10,000 times, generating 10,000 displacement values for each pixel. Subsequently, statistical calculations were performed to determine both the means and standard deviations of these displacement values, resulting in the creation of probability distributions. The predicted displacement probabilities surpassing 5 cm as threshold value were then displayed as landslide susceptibility maps. After generating the susceptibility maps, a comprehensive comparison was conducted based on various evaluation metrics, including confusion matrix, Kappa Coefficient, F1-score, and AUC-ROC values. The analysis revealed that the PSHA-based PGA input performed better than the scenario-based PGA input for co-seismic landslide susceptibility mapping.

Keywords: Monte Carlo simulations; Peak ground acceleration (PGA); Newmark displacement; Co-seismic landslide; Probabilistic analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06517-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:120:y:2024:i:9:d:10.1007_s11069-024-06517-0

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-06517-0

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().