Enhancing natural disaster image classification: an ensemble learning approach with inception and CNN models
Autor: | Kashvi Ankitbhai Sheth, Rujuta Prajakt Kulkarni, G. K. Revathi |
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Jazyk: | angličtina |
Rok vydání: | 2024 |
Předmět: | |
Zdroj: | Geomatics, Natural Hazards & Risk, Vol 15, Iss 1 (2024) |
Druh dokumentu: | article |
ISSN: | 19475705 1947-5713 1947-5705 |
DOI: | 10.1080/19475705.2024.2407029 |
Popis: | 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. |
Databáze: | Directory of Open Access Journals |
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