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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.12871 |
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| _version_ | 1866929282297626624 |
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| author | Maggioros, Spiros Tsalkitzis, Nikos |
| author_facet | Maggioros, Spiros Tsalkitzis, Nikos |
| contents | Convolutional Neural Networks (CNNs) have proven instrumental across various computer science domains, enabling advancements in object detection, classification, and anomaly detection. This paper explores the application of CNNs to analyze geospatial data specifically for identifying wildfire-affected areas. Leveraging transfer learning techniques, we fine-tuned CNN hyperparameters and integrated the Canadian Fire Weather Index (FWI) to assess moisture conditions. The study establishes a methodology for computing wildfire risk levels on a scale of 0 to 5, dynamically linked to weather patterns. Notably, through the integration of transfer learning, the CNN model achieved an impressive accuracy of 95\% in identifying burnt areas. This research sheds light on the inner workings of CNNs and their practical, real-time utility in predicting and mitigating wildfires. By combining transfer learning and CNNs, this study contributes a robust approach to assess burnt areas, facilitating timely interventions and preventative measures against conflagrations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12871 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wildfire danger prediction optimization with transfer learning Maggioros, Spiros Tsalkitzis, Nikos Machine Learning Convolutional Neural Networks (CNNs) have proven instrumental across various computer science domains, enabling advancements in object detection, classification, and anomaly detection. This paper explores the application of CNNs to analyze geospatial data specifically for identifying wildfire-affected areas. Leveraging transfer learning techniques, we fine-tuned CNN hyperparameters and integrated the Canadian Fire Weather Index (FWI) to assess moisture conditions. The study establishes a methodology for computing wildfire risk levels on a scale of 0 to 5, dynamically linked to weather patterns. Notably, through the integration of transfer learning, the CNN model achieved an impressive accuracy of 95\% in identifying burnt areas. This research sheds light on the inner workings of CNNs and their practical, real-time utility in predicting and mitigating wildfires. By combining transfer learning and CNNs, this study contributes a robust approach to assess burnt areas, facilitating timely interventions and preventative measures against conflagrations. |
| title | Wildfire danger prediction optimization with transfer learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.12871 |