Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105839
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Peng, L | - |
dc.creator | Sun, Y | - |
dc.creator | Zhan, Z | - |
dc.creator | Shi, W | - |
dc.creator | Zhang, M | - |
dc.date.accessioned | 2024-04-23T04:31:43Z | - |
dc.date.available | 2024-04-23T04:31:43Z | - |
dc.identifier.issn | 1947-5705 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105839 | - |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.rights | © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group | en_US |
dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. | en_US |
dc.rights | The following publication Peng, L., Sun, Y., Zhan, Z., Shi, W., & Zhang, M. (2023). FR-weighted GeoDetector for landslide susceptibility and driving factors analysis. Geomatics, Natural Hazards and Risk, 14(1), 2205001 is available at https://doi.org/10.1080/19475705.2023.2205001. | en_US |
dc.subject | Correlation analysis | en_US |
dc.subject | Driving factors | en_US |
dc.subject | GeoDetector | en_US |
dc.subject | Landslide susceptibility | en_US |
dc.title | FR-weighted GeoDetector for landslide susceptibility and driving factors analysis | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 14 | - |
dc.identifier.issue | 1 | - |
dc.identifier.doi | 10.1080/19475705.2023.2205001 | - |
dcterms.abstract | Landslide susceptibility analysis is an essential tool for landslide hazard management. Correlation analysis of the driving factors before landslide susceptibility analysis is crucial to obtain more accurate results and higher computational efficiency. This article presents an FR-weighted GeoDetector, which can, at different gridding scales, stably screen out the driving factors most relevant to historical landslides in the study area compared to the performance of the original GeoDetector. The correlation analysis result shows that the most relevant seven conditioning factors to historical landslides in the study area are: lithology, distance to road, elevation, slope, STI, SPI, and distance to faults. Four machine learning models (logistic regression [LR], random forest [RF], artificial neural network [ANN], and Xgboost) are implemented for landslide susceptibility analysis, demonstrating that such models can achieve higher accuracy with features filtered by the FR-weighted GeoDetector than with all features. The Xgboost models trained on seven and 12 features were used to generate landslide susceptibility maps. The overlay with historical landslides showed that the models trained on seven features generated a more reasonable landslide susceptibility map, proving that selecting crucial landslide conditioning factors is a better solution than using a full range of landslide conditioning factors. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Geomatics, natural hazards and risk, 2023, v. 14, no. 1, 2205001 | - |
dcterms.isPartOf | Geomatics, natural hazards and risk | - |
dcterms.issued | 2023 | - |
dc.identifier.scopus | 2-s2.0-85158052021 | - |
dc.identifier.eissn | 1947-5713 | - |
dc.identifier.artn | 2205001 | - |
dc.description.validate | 202404 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Polytechnic University; Smart Cities Research Institute, The Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Peng_FR-weighted_GeoDetector_Landslide.pdf | 5.09 MB | Adobe PDF | View/Open |
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