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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.
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.
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.
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