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

Abstract

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.

Risks of Drone Use in Light of Literature Studies

“…Therefore, many publications in this group refer to combined autonomous data collection and analysis systems based on drones and AI. Of particular relevance is the use of such solutions for monitoring natural disasters in progress, such as fire [ 51 , 52 , 53 ] and flooding [ 43 , 52 , 53 , 54 , 55 , 56 ], in which the decisions made can save lives. The second combination often found in publications about drones used for monitoring is the combination of drones with laser scanner technology (LiDAR) for surveying.…”

“…Identifying areas at risk and monitoring the occurrence and spread of flood [43,[52][53][54][55][56] Identifying areas at risk and monitoring the occurrence and spread of fire [51][52][53] Monitoring the generation of destructive primary lahars responsible for the volcanic eruption [ Monitoring the driving and dangerous behaviour of cyclists [90] Water environment…”

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

“…Elsewhere, in military applications, UAVs with their highly versatile nature and reliability (i.e., remote control ability, durable flight periods, etc. ), are found to be ideal for reconnaissance [4], border patrol [5], and disaster response [6]. Aside from agricultural-and military-based applications, environmental processes also benefit from UAVs, such that ecosystems can be remotely surveyed [7], wildlife can be dynamically tracked [8], and crucial data for conservation efforts and disaster management can be better gathered [9].…”

Efficiency of Using Drones and Boats in Monitoring the Security of the Pang Pang Bay Water Aerodrome Area

“…Efficiency in supervision can have a direct impact on decision-making in managing risks and overcoming emergency situations (Kristina et al, 2022). Therefore, a deeper understanding of the potential of each monitoring tool can help develop better guidelines and protocols in diverse situations (Alsumayt et al, 2023;Alwateer et al, 2019;Behjati et al, 2021;King, 2014).…”