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Gargote, Shraddha Eknath (2024) Real-Time Wildfire Progression Analysis and Prediction using Hybrid Model. Masters thesis, Dublin, National College of Ireland.
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Abstract
The wildfire identification and propagation prediction is a necessary inclusion that allows for the realistic projection of the fire. Wildfires may cause drought, loss of vegetation, loss of property, rise in greenhouse gases, and loss of a wide range of wildlife and human lives too. To reduce this risk, there should be a mechanism where we can detect the fire in the initial stage of raging and predict its progression towards a wildfire to control its movement and stop it. Most of the conventional methods use machine learning models to predict the presence of a fire. This doesn’t help much; we need to track the propagation path of the fire to save more lives and stocks. Hence, to achieve this, the proposed model utilizes a support vector machine for the classification of the fire in real-time images grabbed from NASA’s repository for the given latitude and longitude. Thereafter, the YOLO model is used to detect the wildfire in the obtained satellite image. Finally, the deployment of the LSTM model enables us to provide the progression track of the wildfire.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Mulwa, Catherine UNSPECIFIED |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science S Agriculture > SF Animal culture Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 18 Aug 2025 13:37 |
| Last Modified: | 18 Aug 2025 13:37 |
| URI: | https://norma.ncirl.ie/id/eprint/8565 |
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