Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones
Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intellig...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/11/5148 |
| _version_ | 1797596758443819008 |
|---|---|
| author | Albandari Alsumayt Nahla El-Haggar Lobna Amouri Zeyad M. Alfawaer Sumayh S. Aljameel |
| author_facet | Albandari Alsumayt Nahla El-Haggar Lobna Amouri Zeyad M. Alfawaer Sumayh S. Aljameel |
| author_sort | Albandari Alsumayt |
| collection | DOAJ |
| description | Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology. |
| first_indexed | 2024-03-11T02:57:32Z |
| format | Article |
| id | doaj.art-46f9af41014d427e8b9336c4019f930d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:57:32Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-46f9af41014d427e8b9336c4019f930d2023-11-18T08:33:12ZengMDPI AGSensors1424-82202023-05-012311514810.3390/s23115148Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using DronesAlbandari Alsumayt0Nahla El-Haggar1Lobna Amouri2Zeyad M. Alfawaer3Sumayh S. Aljameel4Computer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaComputer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaComputer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaComputer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaSaudi Aramco Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaGlobal warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.https://www.mdpi.com/1424-8220/23/11/5148UAVsIPFSFDSSDeepALPHESGD |
| spellingShingle | Albandari Alsumayt Nahla El-Haggar Lobna Amouri Zeyad M. Alfawaer Sumayh S. Aljameel Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones Sensors UAVs IPFS FDSS DeepAL PHE SGD |
| title | Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones |
| title_full | Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones |
| title_fullStr | Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones |
| title_full_unstemmed | Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones |
| title_short | Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones |
| title_sort | smart flood detection with ai and blockchain integration in saudi arabia using drones |
| topic | UAVs IPFS FDSS DeepAL PHE SGD |
| url | https://www.mdpi.com/1424-8220/23/11/5148 |
| work_keys_str_mv | AT albandarialsumayt smartflooddetectionwithaiandblockchainintegrationinsaudiarabiausingdrones AT nahlaelhaggar smartflooddetectionwithaiandblockchainintegrationinsaudiarabiausingdrones AT lobnaamouri smartflooddetectionwithaiandblockchainintegrationinsaudiarabiausingdrones AT zeyadmalfawaer smartflooddetectionwithaiandblockchainintegrationinsaudiarabiausingdrones AT sumayhsaljameel smartflooddetectionwithaiandblockchainintegrationinsaudiarabiausingdrones |