Landslide Susceptibility Prediction Adaptive to Triggering Events
Landslide detection and susceptibility prediction are valuable tools for disaster prevention. Despite there being many various solutions to accomplish these tasks, they all generally depend on topographic features of the environment. However, there are not many solutions that can adapt to triggering...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Published: |
Massachusetts Institute of Technology
2023
|
| Online Access: | https://hdl.handle.net/1721.1/151541 |
| _version_ | 1811098097185456128 |
|---|---|
| author | Adebi, Ikechukwu Daniel |
| author2 | Angel, Marcela |
| author_facet | Angel, Marcela Adebi, Ikechukwu Daniel |
| author_sort | Adebi, Ikechukwu Daniel |
| collection | MIT |
| description | Landslide detection and susceptibility prediction are valuable tools for disaster prevention. Despite there being many various solutions to accomplish these tasks, they all generally depend on topographic features of the environment. However, there are not many solutions that can adapt to triggering events such as hurricanes, earthquakes, or volcanic eruptions. This lack of adaptability can greatly limit the performance of the algorithms designed to solve these problems, which, in turn, makes it difficult for emergency managers and responders in the area to prepare for these events appropriately. This work experiments with various kinds of machine learning models and analyzes the effects of incorporating dynamic features based on triggering events in the training process. Ultimately, the final versions of the best performing models produced in this thesis will be deployed as a part of a landslide monitoring system to be used in Mocoa, Colombia. This system is being adapted and developed for the Drones/UAVs for Equitable Climate Change Adaptation (DECCA) project run by MIT’s Environmental Solutions Initiative. |
| first_indexed | 2024-09-23T17:09:51Z |
| format | Thesis |
| id | mit-1721.1/151541 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T17:09:51Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1515412023-08-01T03:01:26Z Landslide Susceptibility Prediction Adaptive to Triggering Events Adebi, Ikechukwu Daniel Angel, Marcela Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Landslide detection and susceptibility prediction are valuable tools for disaster prevention. Despite there being many various solutions to accomplish these tasks, they all generally depend on topographic features of the environment. However, there are not many solutions that can adapt to triggering events such as hurricanes, earthquakes, or volcanic eruptions. This lack of adaptability can greatly limit the performance of the algorithms designed to solve these problems, which, in turn, makes it difficult for emergency managers and responders in the area to prepare for these events appropriately. This work experiments with various kinds of machine learning models and analyzes the effects of incorporating dynamic features based on triggering events in the training process. Ultimately, the final versions of the best performing models produced in this thesis will be deployed as a part of a landslide monitoring system to be used in Mocoa, Colombia. This system is being adapted and developed for the Drones/UAVs for Equitable Climate Change Adaptation (DECCA) project run by MIT’s Environmental Solutions Initiative. M.Eng. 2023-07-31T19:47:19Z 2023-07-31T19:47:19Z 2023-06 2023-06-06T16:35:54.003Z Thesis https://hdl.handle.net/1721.1/151541 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Adebi, Ikechukwu Daniel Landslide Susceptibility Prediction Adaptive to Triggering Events |
| title | Landslide Susceptibility Prediction Adaptive to
Triggering Events |
| title_full | Landslide Susceptibility Prediction Adaptive to
Triggering Events |
| title_fullStr | Landslide Susceptibility Prediction Adaptive to
Triggering Events |
| title_full_unstemmed | Landslide Susceptibility Prediction Adaptive to
Triggering Events |
| title_short | Landslide Susceptibility Prediction Adaptive to
Triggering Events |
| title_sort | landslide susceptibility prediction adaptive to triggering events |
| url | https://hdl.handle.net/1721.1/151541 |
| work_keys_str_mv | AT adebiikechukwudaniel landslidesusceptibilitypredictionadaptivetotriggeringevents |