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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 tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0003-4895-5300
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2024 (Engelska)Ingår i: Discover Applied Sciences, E-ISSN 3004-9261, Vol. 6, nr 1, artikel-id 7Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Springer Nature , 2024. Vol. 6, nr 1, artikel-id 7
Nyckelord [en]
Landslide risk assessment, Landslide susceptibility, Machine learning, Vulnerability index
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Geoteknik
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Drift och underhållsteknik
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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
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Validerad;2024;Nivå 1;2024-04-02 (marisr);

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Tillgänglig från: 2024-04-02 Skapad: 2024-04-02 Senast uppdaterad: 2024-04-02Bibliografiskt granskad

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Najeh, TaoufikGamil, Yaser

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