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Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones.

Albandari Alsumayt1, Nahla El-Haggar1, Lobna Amouri1

  • 1Computer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

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Summary

A secure Flood Detection Secure System (FDSS) using artificial intelligence (AI) and blockchain technology enhances flood monitoring in Saudi Arabia. This system improves data security and accuracy for disaster management.

Keywords:
AIDeepALFDSSIPFSIoDIoTMECPHESGDUAVsblockchainhomomorphic

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Area of Science:

  • Environmental Science and Engineering
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Global warming and climate change exacerbate natural disasters, with floods posing significant risks requiring rapid management.
  • Emerging technologies like artificial intelligence (AI) and unmanned aerial vehicles (UAVs) offer potential for improved emergency response and information dissemination.
  • Existing flood detection methods face challenges in security, privacy, communication costs, and handling large data volumes.

Purpose of the Study:

  • To propose a secure Flood Detection Secure System (FDSS) for flood monitoring in Saudi Arabia.
  • To leverage deep active learning (DeepAL) within a federated learning framework to enhance detection accuracy while minimizing communication overhead.
  • To integrate blockchain and encryption techniques for robust data security, privacy preservation, and efficient data management.

Main Methods:

  • Development of a Flood Detection Secure System (FDSS) employing a deep active learning (DeepAL) classification model.
  • Implementation of federated learning with blockchain technology, utilizing partially homomorphic encryption (PHE) and stochastic gradient descent (SGD).
  • Integration of InterPlanetary File System (IPFS) to manage storage limitations and high data gradients inherent in blockchain transmissions.

Main Results:

  • The FDSS effectively estimates flooded areas and monitors changes in dam water levels, providing crucial threat assessment.
  • Federated learning with PHE ensures privacy-preserving, ciphertext-level model aggregation and filtering, verifying local models securely.
  • The system demonstrates enhanced security, preventing data compromise and alteration by malicious actors.

Conclusions:

  • The proposed FDSS offers a secure, adaptable, and efficient method for flood detection and management, particularly in remote regions.
  • The integration of AI and blockchain technology provides a robust solution for enhancing disaster response strategies.
  • Recommendations are provided for Saudi Arabian decision-makers and administrators to address the increasing threat of flooding.