Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential
This review article aims to discuss the current status and future potential of Geographic Information Systems (GIS) in map-based technology of tsunami risk management, especially in seismically active, well-known tsunami regions of the world. It presents GIS technologies for hazard mapping, risk ass...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/07/e3sconf_errachidia2024_04025.pdf |
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| author | Tahri Ayoub Beroho Mohamed Tichli Soufiane El Talibi Hajar El Moussaoui Said Aboumaria Khadija |
| author_facet | Tahri Ayoub Beroho Mohamed Tichli Soufiane El Talibi Hajar El Moussaoui Said Aboumaria Khadija |
| author_sort | Tahri Ayoub |
| collection | DOAJ |
| description | This review article aims to discuss the current status and future potential of Geographic Information Systems (GIS) in map-based technology of tsunami risk management, especially in seismically active, well-known tsunami regions of the world. It presents GIS technologies for hazard mapping, risk assessment, and information generation for disaster-response operations. These are important tools for accurately mapping vulnerable areas by integrating real-time and historical data to develop accurate forecasts for possible tsunamis. Demographic and geographic data were also analyzed by GIS to determine the optimum route to develop evacuation strategies. A set of case studies demonstrates how GIS improves community resilience by supporting informed decision-making. In addition, suggestions are made for how future steeps as the integration of machine learning techniques as emerging tools for analyzing and classifying complex and vast datasets, which may enhance GIS applications in tsunami risk management to improve the accuracy and utility of these tools. |
| format | Article |
| id | doaj-art-dab5eb9ae1ca4da18f02fd7ea8b6a4ba |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-dab5eb9ae1ca4da18f02fd7ea8b6a4ba2025-02-05T10:49:24ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016070402510.1051/e3sconf/202560704025e3sconf_errachidia2024_04025Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potentialTahri Ayoub0Beroho Mohamed1Tichli Soufiane2El Talibi Hajar3El Moussaoui Said4Aboumaria Khadija5Research and Development in Applied Geosciences Laboratory (R&DGéoAp), Department of Earth Sciences, Abdelmalek Essaâdi UniversityResearch and Development in Applied Geosciences Laboratory (R&DGéoAp), Department of Earth Sciences, Abdelmalek Essaâdi UniversityResearch and Development in Applied Geosciences Laboratory (R&DGéoAp), Department of Earth Sciences, Abdelmalek Essaâdi UniversityResearch and Development in Applied Geosciences Laboratory (R&DGéoAp), Department of Earth Sciences, Abdelmalek Essaâdi UniversityResearch and Development in Applied Geosciences Laboratory (R&DGéoAp), Department of Earth Sciences, Abdelmalek Essaâdi UniversityResearch and Development in Applied Geosciences Laboratory (R&DGéoAp), Department of Earth Sciences, Abdelmalek Essaâdi UniversityThis review article aims to discuss the current status and future potential of Geographic Information Systems (GIS) in map-based technology of tsunami risk management, especially in seismically active, well-known tsunami regions of the world. It presents GIS technologies for hazard mapping, risk assessment, and information generation for disaster-response operations. These are important tools for accurately mapping vulnerable areas by integrating real-time and historical data to develop accurate forecasts for possible tsunamis. Demographic and geographic data were also analyzed by GIS to determine the optimum route to develop evacuation strategies. A set of case studies demonstrates how GIS improves community resilience by supporting informed decision-making. In addition, suggestions are made for how future steeps as the integration of machine learning techniques as emerging tools for analyzing and classifying complex and vast datasets, which may enhance GIS applications in tsunami risk management to improve the accuracy and utility of these tools.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/07/e3sconf_errachidia2024_04025.pdfgismachine learning techniquestsunamirisk managementhazard mapping |
| spellingShingle | Tahri Ayoub Beroho Mohamed Tichli Soufiane El Talibi Hajar El Moussaoui Said Aboumaria Khadija Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential E3S Web of Conferences gis machine learning techniques tsunami risk management hazard mapping |
| title | Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential |
| title_full | Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential |
| title_fullStr | Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential |
| title_full_unstemmed | Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential |
| title_short | Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential |
| title_sort | combining gis and machine learning for enhanced tsunami risk management a review of current approaches and unexplored future potential |
| topic | gis machine learning techniques tsunami risk management hazard mapping |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/07/e3sconf_errachidia2024_04025.pdf |
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