Abstract
Natural disasters not only cause death and property destruction around the world but the severity of these disasters is also reflected in many other forms like the economic loss, loss of lives, and the ability of populations to rebuild. This paper focuses on using machine learning, deep learning, and computer vision to detect natural disasters. Previously, many attempts have been made to detect natural disasters using different techniques including the use of sensors, satellites, drones, crowdsourcing, and basic ML models and reducing the severity of these disasters. But natural disaster detection still faces challenges due to the limitations of the above techniques like high cost of equipment and training time, lower accuracy, etc. Therefore, in order to combat these issues, transfer learning models are being used, namely a few architectures from VGG, ResNet, and EfficientNet. Many different values for epochs and dataset sizes were experimented with, as well as different configurations for the hidden layers and optimizers to improve the classification accuracy. From the results obtained after training the models, ResNet50 gave the best results with an accuracy of 96.35%.
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Bosco, J., Yavagal, L., Srinivas, L.T., Katabatthina, M.K., Kasturi, N. (2023). Detection of Natural Disasters Using Machine Learning and Computer Vision by Replacing the Need of Sensors. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_50
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DOI: https://doi.org/10.1007/978-981-99-5166-6_50
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