Landslide risk assessment based on susceptibility and vulnerability

Mosaffaie, Jamal; Jam, Amin Salehpour; Sarfaraz, Faramarz

doi:10.1007/s10668-023-03093-4

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…Besides, Ram & Gupta (2022) conducted vulnerability assessments based on the landslide hazard, physical and environmental aspects. Also, a study by Mosaffaie et al (2023) analyzed potential resources that were potentially exposed to landslide. Nevertheless, some vulnerability studies were conducted partially, for example (1) physical vulnerability that assessed landslide intensity and building vulnerability (Tiwari et al, 2022); (2) social vulnerability that described disparities in social groups, based on socio-economic indicators (Nor Diana et al, 2021;Sangeeta & Maheshwari, 2022;Wang et al, 2022); and (3) environmental vulnerability that examined land cover aspects (Hani'ah et al, 2017).…”

Section: Article Infomentioning

confidence: 99%

Spatial-Based Landslide Vulnerability Index Assessment in Bogor Area, Indonesia

Trisnafiah,

Trinugroho,

Astisiasari

et al. 2024

Geos. Ind.

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Within the concept of disaster risk, vulnerability is one of the key determinants. It acknowledges the degree of unsafe conditions in a susceptible zone so that mitigation measures and disaster resilience can be enforced. Bogor area is the most susceptible region to landslides with more than 485 landslide events since 2012. However, there is still inadequate information on its vulnerability to landslides, as the risk reduction challenge per se, is a long-run task. Correspondingly, this study aims to measure the degree of vulnerability to landslides in Bogor area from four focal points (i.e.: social, physical, economic, and environmental dimensions), through the proxy of Vulnerability Index (VI). This study employs a GIS-based spatial analysis on a sub-district level. The result shows that although having high records on landslide events, Bogor area mainly retrieves low VI. In general, Bogor area has low VI [0.347–0.454] on 26 sub-districts. Moreover, 13 sub-districts attain medium VI (0.454–0.562], and 7 sub-districts achieve high VI (0.562–0.670]. Sub-districts that have high VI are: Bogor Tengah, Bogor Barat, Bogor Selatan, Cibinong, Bojonggede, Ciomas, and Bogor Utara; which are mainly promoted by the high indexes on social and physical vulnerabilities. Nevertheless, further study is still needed to extend the knowledge of relationship between landslide susceptibility and this vulnerability result, by using a more extensive and longer data series. That is especially in accord with taking the appropriate mitigation measures in spatial planning and landslide risk management.

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Section: Article Infomentioning

confidence: 99%

Spatial-Based Landslide Vulnerability Index Assessment in Bogor Area, Indonesia

Trisnafiah,

Trinugroho,

Astisiasari

et al. 2024

Geos. Ind.

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“…A higher AUC value indicates better predictive performance. OA measures the accuracy of correctly predicting sample points and can be calculated using Equation (5).…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

“…Therefore, selecting appropriate earthquake-related factors to express seismic characteristics is very important. Peak ground acceleration (PGA) [4][5][6] and Arias intensity (I a ) [7][8][9] are most widely used to capture the relationships between seismic landslides and earthquakes. However, the seismic features they characterize are relatively superficial.…”

Section: Introductionmentioning

confidence: 99%

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

Li,

Ming,

Zhang

et al. 2024

Remote Sensing

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Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.

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