Leveraging AI and Cloud Technology for Disaster Prediction and Management
DOI:
https://doi.org/10.15662/IJEETR.2023.0506017Keywords:
Aim, cloud computing, computer science, computing, disaster management, information technology, information systems, machine learning, operational research, services, systems, technologies, artificial intelligence, big data, bioinformatics, security, data management, data processing, disaster prediction, disaster response, warning, weather forecasting, sensor networks, remote sensing, image processing, decision support system, object detection, deep learning, situational awareness, science technology, educationAbstract
Cloud Computing and Artificial Intelligence (AI) are two of the widely used technological domains in the present world. Clouds are considered as a base for Disaster Management Support Systems. In the cloud environment, people have enormous computing, storage, and communication capabilities at ease so that they could collect large amounts of data from different sources when disaster incidents occur, and later analyze these data and build knowledge. Data and information serve as the critical backbone of Disaster Management. AI techniques and systems are used in Disaster Management area for Disaster prediction and monitoring. AI is useful for searching damaged buildings and planning in dangerous environments of rescue with machine-to-machine or human-machine collaboration. Supported by cloud environment, Disaster Management Support Systems provide different real-time data analysis services.
In areas of the world that suffer from severe weather driven-hazards such as storm surges, flooding, or heavy snow and wind, a greater effort mediated by cooperatives and National Disaster Management Agencies is needed. There is an opportunity to develop Cloud Systems that bring together hydrological, meteorological, and impact models at high spatial resolution for forecast and early warning in these regions. Such a service will be essential not only for local, national, and regional receivers of the information but also for adjacent areas that may be indirectly affected by the potential trans-boundary effects of severe weather events.
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