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A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran.
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran.ORCID iD: 0000-0001-5091-6947
Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 6617715175, Iran.ORCID iD: 0000-0001-9668-8687
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-6790-2653
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2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 4, article id 1573Article in journal (Refereed) Published
Abstract [en]

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 22, no 4, article id 1573
Keywords [en]
landslide susceptibility, extreme learning machine, deep belief network, genetic algorithm, GIS, Iran
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-89336DOI: 10.3390/s22041573ISI: 000775434600001PubMedID: 35214473Scopus ID: 2-s2.0-85124678085OAI: oai:DiVA.org:ltu-89336DiVA, id: diva2:1638923
Note

Validerad;2022;Nivå 2;2022-02-18 (sofila);

Funder: University of Kurdistan, Iran (grant no. 11-99-4469)

Available from: 2022-02-18 Created: 2022-02-18 Last updated: 2023-09-05Bibliographically approved

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Al-Ansari, Nadhir

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