An objective absence data sampling method for landslide susceptibility mapping
Abstract The accuracy and quality of the landslide susceptibility map depend on the available landslide locations and the sampling strategy for absence data (non-landslide locations). In this study, we propose an objective method to determine the critical value for sampling absence data based on Mah...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-28991-5 |
| _version_ | 1811171753574006784 |
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| author | Yasin Wahid Rabby Yingkui Li Haileab Hilafu |
| author_facet | Yasin Wahid Rabby Yingkui Li Haileab Hilafu |
| author_sort | Yasin Wahid Rabby |
| collection | DOAJ |
| description | Abstract The accuracy and quality of the landslide susceptibility map depend on the available landslide locations and the sampling strategy for absence data (non-landslide locations). In this study, we propose an objective method to determine the critical value for sampling absence data based on Mahalanobis distances (MD). We demonstrate this method on landslide susceptibility mapping of three subdistricts (Upazilas) of the Rangamati district, Bangladesh, and compare the results with the landslide susceptibility map produced based on the slope-based absence data sampling method. Using the 15 landslide causal factors, including slope, aspect, and plan curvature, we first determine the critical value of 23.69 based on the Chi-square distribution with 14 degrees of freedom. This critical value was then used to determine the sampling space for 261 random absence data. In comparison, we chose another set of the absence data based on a slope threshold of < 3°. The landslide susceptibility maps were then generated using the random forest model. The Receiver Operating Characteristic (ROC) curves and the Kappa index were used for accuracy assessment, while the Seed Cell Area Index (SCAI) was used for consistency assessment. The landslide susceptibility map produced using our proposed method has relatively high model fitting (0.87), prediction (0.85), and Kappa values (0.77). Even though the landslide susceptibility map produced by the slope-based sampling also has relatively high accuracy, the SCAI values suggest lower consistency. Furthermore, slope-based sampling is highly subjective; therefore, we recommend using MD -based absence data sampling for landslide susceptibility mapping. |
| first_indexed | 2024-04-10T17:19:15Z |
| format | Article |
| id | doaj.art-49a16bec6bf9425391629bb95bdbde4c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T17:19:15Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-49a16bec6bf9425391629bb95bdbde4c2023-02-05T12:10:41ZengNature PortfolioScientific Reports2045-23222023-01-0113111510.1038/s41598-023-28991-5An objective absence data sampling method for landslide susceptibility mappingYasin Wahid Rabby0Yingkui Li1Haileab Hilafu2Department of Engineering, Wake Forest UniversityDepartment of Geography & Sustainability, University of TennesseeDepartment of Business Analytics and Statistics, University of TennesseeAbstract The accuracy and quality of the landslide susceptibility map depend on the available landslide locations and the sampling strategy for absence data (non-landslide locations). In this study, we propose an objective method to determine the critical value for sampling absence data based on Mahalanobis distances (MD). We demonstrate this method on landslide susceptibility mapping of three subdistricts (Upazilas) of the Rangamati district, Bangladesh, and compare the results with the landslide susceptibility map produced based on the slope-based absence data sampling method. Using the 15 landslide causal factors, including slope, aspect, and plan curvature, we first determine the critical value of 23.69 based on the Chi-square distribution with 14 degrees of freedom. This critical value was then used to determine the sampling space for 261 random absence data. In comparison, we chose another set of the absence data based on a slope threshold of < 3°. The landslide susceptibility maps were then generated using the random forest model. The Receiver Operating Characteristic (ROC) curves and the Kappa index were used for accuracy assessment, while the Seed Cell Area Index (SCAI) was used for consistency assessment. The landslide susceptibility map produced using our proposed method has relatively high model fitting (0.87), prediction (0.85), and Kappa values (0.77). Even though the landslide susceptibility map produced by the slope-based sampling also has relatively high accuracy, the SCAI values suggest lower consistency. Furthermore, slope-based sampling is highly subjective; therefore, we recommend using MD -based absence data sampling for landslide susceptibility mapping.https://doi.org/10.1038/s41598-023-28991-5 |
| spellingShingle | Yasin Wahid Rabby Yingkui Li Haileab Hilafu An objective absence data sampling method for landslide susceptibility mapping Scientific Reports |
| title | An objective absence data sampling method for landslide susceptibility mapping |
| title_full | An objective absence data sampling method for landslide susceptibility mapping |
| title_fullStr | An objective absence data sampling method for landslide susceptibility mapping |
| title_full_unstemmed | An objective absence data sampling method for landslide susceptibility mapping |
| title_short | An objective absence data sampling method for landslide susceptibility mapping |
| title_sort | objective absence data sampling method for landslide susceptibility mapping |
| url | https://doi.org/10.1038/s41598-023-28991-5 |
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