Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress
In the current work, authors reviewed the latest research results in landslide susceptibility mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of collected publications, the review was classified into four sections based on their complexity: single-model approache...
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MDPI AG
2024-12-01
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| author | Muratbek Kudaibergenov Serik Nurakynov Berik Iskakov Gulnara Iskaliyeva Yelaman Maksum Elmira Orynbassarova Bakytzhan Akhmetov Nurmakhambet Sydyk |
| author_facet | Muratbek Kudaibergenov Serik Nurakynov Berik Iskakov Gulnara Iskaliyeva Yelaman Maksum Elmira Orynbassarova Bakytzhan Akhmetov Nurmakhambet Sydyk |
| author_sort | Muratbek Kudaibergenov |
| collection | DOAJ |
| description | In the current work, authors reviewed the latest research results in landslide susceptibility mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of collected publications, the review was classified into four sections based on their complexity: single-model approaches, enhanced models with optimization, ensemble models, and hybrid models. Each category offers distinct advantages and is suited to specific geographic and data conditions, enabling the selection of an optimal model type based on the complexity and requirements of the mapping task. Among models, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and multilayer perception (MLP) are used as the baseline to compare any new model introduced to develop LSM. Moreover, compared to previous review works, the number of LSM conditioning factors used in AI models are significantly increased, up to 122 factors. Their relation to the AI models is illustrated using Sankey diagram, while a radar chart is used to further visualize the dataset size per reviewed work for comparative purposes. In the main part of the current review work, the main findings are summarized into a table form, where the reader can find the overall relations between landslide conditioning factors, landslide dataset size, applied AI models, and their accuracy on predicting LSM for selected geographical locations. In terms of the regions, Asia is leading in the application of AI models to generate LSM, and in such regions with dense populations falling into higher landslide risk categories, there are more ongoing research activities, using modern AI methods. This trend underscores the increased use of AI in disaster management, with implications for improving practical applications, such as early warning systems and informing policy decisions aimed at risk reduction in vulnerable areas. |
| format | Article |
| id | doaj-art-52808e1689be4fae8d4ca63a16b87f00 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-52808e1689be4fae8d4ca63a16b87f002025-01-10T13:20:01ZengMDPI AGRemote Sensing2072-42922024-12-011713410.3390/rs17010034Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent ProgressMuratbek Kudaibergenov0Serik Nurakynov1Berik Iskakov2Gulnara Iskaliyeva3Yelaman Maksum4Elmira Orynbassarova5Bakytzhan Akhmetov6Nurmakhambet Sydyk7Institute of Ionosphere, Almaty 050000, KazakhstanInstitute of Ionosphere, Almaty 050000, KazakhstanInstitute of Ionosphere, Almaty 050000, KazakhstanInstitute of Ionosphere, Almaty 050000, KazakhstanDepartment of Chemical Engineering, University of Birmingham, Birmingham B15 2TT, UKDepartment of Surveying and Geodesy, Satbayev University, Almaty 050000, KazakhstanInstitute of Ionosphere, Almaty 050000, KazakhstanInstitute of Ionosphere, Almaty 050000, KazakhstanIn the current work, authors reviewed the latest research results in landslide susceptibility mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of collected publications, the review was classified into four sections based on their complexity: single-model approaches, enhanced models with optimization, ensemble models, and hybrid models. Each category offers distinct advantages and is suited to specific geographic and data conditions, enabling the selection of an optimal model type based on the complexity and requirements of the mapping task. Among models, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and multilayer perception (MLP) are used as the baseline to compare any new model introduced to develop LSM. Moreover, compared to previous review works, the number of LSM conditioning factors used in AI models are significantly increased, up to 122 factors. Their relation to the AI models is illustrated using Sankey diagram, while a radar chart is used to further visualize the dataset size per reviewed work for comparative purposes. In the main part of the current review work, the main findings are summarized into a table form, where the reader can find the overall relations between landslide conditioning factors, landslide dataset size, applied AI models, and their accuracy on predicting LSM for selected geographical locations. In terms of the regions, Asia is leading in the application of AI models to generate LSM, and in such regions with dense populations falling into higher landslide risk categories, there are more ongoing research activities, using modern AI methods. This trend underscores the increased use of AI in disaster management, with implications for improving practical applications, such as early warning systems and informing policy decisions aimed at risk reduction in vulnerable areas.https://www.mdpi.com/2072-4292/17/1/34AI modellandslide susceptibility mappingconditioning factorsdatasetmodel accuracy |
| spellingShingle | Muratbek Kudaibergenov Serik Nurakynov Berik Iskakov Gulnara Iskaliyeva Yelaman Maksum Elmira Orynbassarova Bakytzhan Akhmetov Nurmakhambet Sydyk Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress Remote Sensing AI model landslide susceptibility mapping conditioning factors dataset model accuracy |
| title | Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress |
| title_full | Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress |
| title_fullStr | Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress |
| title_full_unstemmed | Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress |
| title_short | Application of Artificial Intelligence in Landslide Susceptibility Assessment: Review of Recent Progress |
| title_sort | application of artificial intelligence in landslide susceptibility assessment review of recent progress |
| topic | AI model landslide susceptibility mapping conditioning factors dataset model accuracy |
| url | https://www.mdpi.com/2072-4292/17/1/34 |
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