Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/96561
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.contributor | Research Institute for Land and Space | - |
dc.creator | Shahzad, N | en_US |
dc.creator | Ding, X | en_US |
dc.creator | Abbas, S | en_US |
dc.date.accessioned | 2022-12-07T02:55:26Z | - |
dc.date.available | 2022-12-07T02:55:26Z | - |
dc.identifier.issn | 2076-3417 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/96561 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Shahzad, N., Ding, X., & Abbas, S. (2022). A comparative assessment of machine learning models for landslide susceptibility mapping in the rugged terrain of northern Pakistan. Applied Sciences, 12(5), 2280 is available at https://doi.org/10.3390/app12052280. | en_US |
dc.subject | Gradient-boosting machine | en_US |
dc.subject | Landslide susceptibility | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Maximum entropy | en_US |
dc.subject | Pakistan | en_US |
dc.subject | Random forest | en_US |
dc.subject | Rugged terrain | en_US |
dc.subject | Support vector machine | en_US |
dc.title | A comparative assessment of machine learning models for landslide susceptibility mapping in the rugged terrain of northern Pakistan | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.doi | 10.3390/app12052280 | en_US |
dcterms.abstract | This study investigated the performances of different techniques, including random forest (RF), support vector machine (SVM), maximum entropy (maxENT), gradient-boosting machine (GBM), and logistic regression (LR), for landslide susceptibility mapping (LSM) in the rugged terrain of northern Pakistan. Initially, a landslide inventory of 200 samples was produced along with an additional 200 samples indicating nonlandslide areas and divided into training (70%) and validation (30%) groups using a stratified loop-based random sampling approach. Then, a geospatial database of 12 possible landslide influencing factors (LIFs) was generated, including elevation, slope, aspect, topographic wetness index (TWI), topographic position index (TPI), distance to drainage, distance to fault, distance to road, normalized difference vegetation index (NDVI), rainfall, land cover/land use (LCLU), and a geological map of the study area. None of the LIFs were redundant for the modeling, as indicated by the multicollinearity test (tolerance > 0.1) and information gain ratio (IGR > 0). We extended the evaluation measures of each algorithm from area-under-the-curve (AUC) analysis to the calculation of performance overall (POA) with the help of precision, recall, F1 score, accuracy (ACC), and Matthew’s correlation coefficient (MCC). The results showed that the SVM was the most promising model (AUC = 0.969, POA = 2669) for the LSM, followed by RF (AUC = 0.967, POA = 2656), GBM (AUC = 0.967, POA = 2623), maxENT (AUC = 0.872, POA = 1761), and LR (AUC = 0.836, POA = 1299). It is important to note that the SVM, RF, and GBM were the top performers, with almost similar accuracy. Thus, each of these could be equally effective for LSM and can be used for risk reduction and mitigation measures in the rugged terrain of Pakistan and other regions with similar topography. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Applied sciences, Mar. 2022, v. 12, no. 5, 2280 | en_US |
dcterms.isPartOf | Applied sciences | en_US |
dcterms.issued | 2022-03 | - |
dc.identifier.scopus | 2-s2.0-85125423662 | - |
dc.identifier.artn | 2280 | en_US |
dc.description.validate | 202212 bckw | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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applsci-12-02280-v2.pdf | 18.2 MB | Adobe PDF | View/Open |
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