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Landslide risk assessment integrating susceptibility, hazard, and vulnerability analysis in Northern Pakistan
School of Civil and Resource Engineering, University of Science and Technology Beijing, 100083, Beijing, China.
Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, District Swabi, 23640, Topi, Khyber Pakhtunkhwa, Pakistan.
School of Civil and Resource Engineering, University of Science and Technology Beijing, 100083, Beijing, China.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-4895-5300
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2024 (English)In: Discover Applied Sciences, E-ISSN 3004-9261, Vol. 6, no 1, article id 7Article in journal (Refereed) Published
Abstract [en]

The purpose of this study is to assess the landslide risk for Hunza–Nagar Valley (Northern Pakistan). In this study, different conditioning factors, e.g., topographical, geomorphological, climatic, and geological factors were considered. Two machine learning approaches, i.e., logistic regression and artificial neural network were used to develop landslide susceptibility maps. The accuracy test was carried out using the receiving operative characteristic (ROC) curve. Which showed that the success and prediction rates of LR model is 82.60 and 81.60%, while 77.90 and 75.40%, for the ANN model. Due to the physiographic condition of the area, the rainfall density was considered as the primary triggering factor and landslide index map was generated. Moreover, using the Aster data the land cover (LC) map was developed. The settlements were extracted from the LC map and used as the elements at risk and hence, the vulnerability index was developed. Finally, the landslide risk map (LRM) for the Hunza–Nagar valley was developed. The LRM indicated that 37.25 (20.21 km2) and 47.64% (25.84 km2) of the total settlements lie in low and very high-risk zones. This landslide risk map can help decision-makers for potential land development and landslide countermeasures.

Place, publisher, year, edition, pages
Springer Nature , 2024. Vol. 6, no 1, article id 7
Keywords [en]
Landslide risk assessment, Landslide susceptibility, Machine learning, Vulnerability index
National Category
Geotechnical Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-104927DOI: 10.1007/s42452-024-05646-2Scopus ID: 2-s2.0-85188319075OAI: oai:DiVA.org:ltu-104927DiVA, id: diva2:1848049
Note

Validerad;2024;Nivå 1;2024-04-02 (marisr);

Full text license: CC BY

Available from: 2024-04-02 Created: 2024-04-02 Last updated: 2024-04-02Bibliographically approved

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Najeh, TaoufikGamil, Yaser

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