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Internet of Things and AI for Natural Disaster Management and Prediction

Om Internet of Things and AI for Natural Disaster Management and Prediction

In a world where natural disasters wreak havoc with increasing frequency and severity, the need for accurate prediction and effective management has never been more critical. From earthquakes shattering communities to floods submerging vast regions, these events endanger lives and strain resources and infrastructure to their limits. Yet, amidst this turmoil, traditional forecasting methods often need to catch up, leaving us vulnerable and reactive rather than proactive. This comprehensive academic collection provides a beacon of hope in uncertain circumstances: Internet of Things and AI for Natural Disaster Management and Prediction . By bridging the gap between theory and practice, this book empowers academics, policymakers, and practitioners alike to harness the full potential of machine learning in safeguarding lives and livelihoods.

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  • Språk:
  • Engelska
  • ISBN:
  • 9798369342848
  • Format:
  • Inbunden
  • Utgiven:
  • 7. mars 2024
  • Mått:
  • 178x254x22 mm.
  • Vikt:
  • 880 g.
Leveranstid: 2-4 veckor
Förväntad leverans: 17. december 2024

Beskrivning av Internet of Things and AI for Natural Disaster Management and Prediction

In a world where natural disasters wreak havoc with increasing frequency and severity, the need for accurate prediction and effective management has never been more critical. From earthquakes shattering communities to floods submerging vast regions, these events endanger lives and strain resources and infrastructure to their limits. Yet, amidst this turmoil, traditional forecasting methods often need to catch up, leaving us vulnerable and reactive rather than proactive. This comprehensive academic collection provides a beacon of hope in uncertain circumstances: Internet of Things and AI for Natural Disaster Management and Prediction . By bridging the gap between theory and practice, this book empowers academics, policymakers, and practitioners alike to harness the full potential of machine learning in safeguarding lives and livelihoods.

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