Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models
The core problem of this research is the rapid and accurate classification of natural disasters, which is essential for effective response and mitigation strategies. Existing detection methods are often time-consuming and costly. The purpose of this research is to introduce an innovative approach to...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2407029 |
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| author | Kashvi Ankitbhai Sheth Rujuta Prajakt Kulkarni G. K. Revathi |
| author_facet | Kashvi Ankitbhai Sheth Rujuta Prajakt Kulkarni G. K. Revathi |
| author_sort | Kashvi Ankitbhai Sheth |
| collection | DOAJ |
| description | The core problem of this research is the rapid and accurate classification of natural disasters, which is essential for effective response and mitigation strategies. Existing detection methods are often time-consuming and costly. The purpose of this research is to introduce an innovative approach to the multi-class classification of natural disasters using image data from a Kaggle dataset encompassing Cyclone, Wildfire, Flood, and Earthquake incidents. The method used is an ensemble learning model that combines the strengths of the InceptionV3 model and a custom Convolutional Neural Network (CNN). The result of this study is an ensemble model that achieves a commendable accuracy of 92.79%, surpassing individual models and demonstrating the efficacy of combining diverse features extracted by InceptionV3 and CNN architectures. Additionally, a standalone CNN model is implemented, achieving an accuracy of 88.76%. The research concludes that the ensemble model’s superior performance makes it a valuable tool for the multi-class classification of natural disaster images. |
| format | Article |
| id | doaj-art-48bcdf4eee3d40e2840e2bd37c291334 |
| institution | OA Journals |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-48bcdf4eee3d40e2840e2bd37c2913342025-08-20T02:34:32ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2024.2407029Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN modelsKashvi Ankitbhai Sheth0Rujuta Prajakt Kulkarni1G. K. Revathi2School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaDepartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, IndiaThe core problem of this research is the rapid and accurate classification of natural disasters, which is essential for effective response and mitigation strategies. Existing detection methods are often time-consuming and costly. The purpose of this research is to introduce an innovative approach to the multi-class classification of natural disasters using image data from a Kaggle dataset encompassing Cyclone, Wildfire, Flood, and Earthquake incidents. The method used is an ensemble learning model that combines the strengths of the InceptionV3 model and a custom Convolutional Neural Network (CNN). The result of this study is an ensemble model that achieves a commendable accuracy of 92.79%, surpassing individual models and demonstrating the efficacy of combining diverse features extracted by InceptionV3 and CNN architectures. Additionally, a standalone CNN model is implemented, achieving an accuracy of 88.76%. The research concludes that the ensemble model’s superior performance makes it a valuable tool for the multi-class classification of natural disaster images.https://www.tandfonline.com/doi/10.1080/19475705.2024.2407029Ensemble learning modelInceptionV3convolutional neural network (CNN)cyclonewildfireearthquake |
| spellingShingle | Kashvi Ankitbhai Sheth Rujuta Prajakt Kulkarni G. K. Revathi Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models Geomatics, Natural Hazards & Risk Ensemble learning model InceptionV3 convolutional neural network (CNN) cyclone wildfire earthquake |
| title | Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models |
| title_full | Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models |
| title_fullStr | Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models |
| title_full_unstemmed | Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models |
| title_short | Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models |
| title_sort | enhancing natural disaster image classification an ensemble learning approach with inception and cnn models |
| topic | Ensemble learning model InceptionV3 convolutional neural network (CNN) cyclone wildfire earthquake |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2024.2407029 |
| work_keys_str_mv | AT kashviankitbhaisheth enhancingnaturaldisasterimageclassificationanensemblelearningapproachwithinceptionandcnnmodels AT rujutaprajaktkulkarni enhancingnaturaldisasterimageclassificationanensemblelearningapproachwithinceptionandcnnmodels AT gkrevathi enhancingnaturaldisasterimageclassificationanensemblelearningapproachwithinceptionandcnnmodels |