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Optimizing Flood Risk Management Through Geospatial AI and Remote Sensing
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Author(s): G. Prabhanjana (Christ University, India), Yashas Shetty (Christ University, India), Daksh Vats (Christ University, India)and Rajesh Kanna Rajendran (Christ University, India)
Copyright: 2025
Pages: 22
Source title:
Recent Trends in Geospatial AI
Source Author(s)/Editor(s): Dina Darwish (Ahram Canadian University, Egypt)and Houssem Chemingui (Brest Business School, France & Centre de Recherche en Informatique, Panthéon Sorbonne University, France
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DOI: 10.4018/979-8-3693-8054-3.ch006
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Abstract
Flooding presents an increasing threat to communities globally, intensified by climate change and urban expansion. Effective flood risk management requires precise and timely information to guide decision-making and planning. This chapter explores the use of geospatial artificial intelligence (AI) in combination with remote sensing to enhance flood risk management. Advanced AI techniques are applied to analyze satellite and aerial images, enabling more accurate identification of flood-prone areas and prediction of potential flood events.Machine learning is utilized to integrate historical and real-time data, improving flood prediction models and evaluating the effectiveness of various mitigation strategies. A decision-support system is developed to leverage this technology, providing valuable insights for policymakers, emergency responders, and urban planners. This chapter demonstrate that the integration of geospatial AI and remote sensing can significantly advance flood risk management, offering a more proactive and resilient approach to addressing this critical issue.
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