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Machine Learning Algorithms for Natural Disaster Management
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Author(s): S. Selvanayaki (Dr. Mahalingam College of Engineering and Technology, India), S. Deepa (Dr. Mahalingam College of Engineering and Technology, India)and G. Keerthika (Dr. Mahalingam College of Engineering and Technology, India)
Copyright: 2024
Pages: 24
Source title:
Internet of Things and AI for Natural Disaster Management and Prediction
Source Author(s)/Editor(s): D. Satishkumar (Nehru Institute of Technology, India)and M. Sivaraja (Nehru Institute of Technology, India)
DOI: 10.4018/979-8-3693-4284-8.ch010
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Abstract
The repercussions of natural disasters can be devastating to the local population, and their recurrence is unavoidable. Scientists all throughout the globe are trying to figure out how to reliably predict when these disasters will strike. In order to create early warning systems that can notify communities and individuals in impacted areas, enabling them to take appropriate measures and lessen the disaster's impact, it is required to analyze a variety of environmental, geological, and meteorological elements. When it comes to disaster management, ML algorithms are great for handling big amounts of data that are naturally formed in surroundings and can handle multiple dimensions. A number of disaster management activities, including predicting when and where crowds will evacuate, evaluating social media posts, and managing sustainable development, have found applications for these algorithms. This chapter provides a comprehensive overview of the several machine learning (ML) and deep learning (DL) methods that have been used for managing and predicting natural disasters.
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