AI-DRIVEN FLOOD RESILIENCE: ADVANCED PREDICTIVE MODELING AND RESPONSE OPTIMIZATION FOR FLORIDA - Muhammad Zeeshan; Muhammad Furqan (Spectrum of Engineering Sciences, 2025)
This paper focusses on developing a state-of-the-art AI-driven system specifically tailored to Florida's unique geographical and climate characteristics, aiming to significantly enhance the state's flood resilience capabilities. It is initiated by data gathering and assessment, encompassing recent flood trends, storm surge effects, and booming structures in Florida. In light of the recent devastating floods in Florida, this research takes on new urgency in leveraging AI to revolutionize flood preparedness and response. This will involve incorporating data collection systems unique to Florida, which include high-topography maps, water level sensors, local weather radar, satellite images, and conversations on social media. This paper outlines the findings of the study on the methodology of developing the Florida Flood Resilience System (FFRS) towards improving flood preparedness and response. The data is utilized to establish AI models tailored for the state of Florida, all the while taking into consideration its flood-related characteristics and working with the emergency management organizations to make sure the deployed AI models are compatible with established protocols. Some of the measures that are part of FFRS include developing specific weather-based neighborhood profiles for flood prediction, using satellites and drones for real-time flood mapping, and efficient flood infrastructure management for optimum flood risk control measures. Also, the individual evacuation map and alarm offer specific escape paths and notifications depending on disabilities; on the other hand, the resource management engine offers real-time resource distribution in cases of emergencies. The process of reviewing and paying insurance claims is easier and faster, especially in the aftermath of floods. Last, a continuous learning module contains post-event analyses for floods and provides training for the emergency personnel through AR/VR technologies; all of these advance Florida’s flood preparedness. By focusing our research on the specific challenges highlighted by the recent Florida floods, we aim to develop a highly targeted and immediately applicable AI-driven flood resilience system. This approach not only addresses the urgent needs of flood-prone regions in Florida but also serves as a scalable model for other coastal areas facing similar climate-related challenges. The proposed Florida Flood Resilience System represents a significant leap forward in applied AI for disaster management. It promises to save lives, reduce economic losses, and enhance community resilience in the face of increasing flood risks. This research underscores the critical role of AI in adapting to climate change impacts and sets a new standard for proactive, intelligent flood management systems worldwide. Keywords (Florida flood resilience, AI-driven flood prediction, hyper-local modeling, smart infrastructure management, personalized early warning, climate adaptation, real-time flood mapping, post-flood recovery assistance).