ADVANCING U.S. DISASTER PREPAREDNESS THROUGH AI-ENABLED CLIMATE MONITORING, RISK ANALYTICS, AND EARLY WARNING SYSTEMS

The U.S. coastal populations are challenged to achieve disaster preparedness owing to the rising effects of theclimate change that are sea rise, frequent hurricanes, and floods. The presented paper discusses the disruptiveopportunities of Artificial Intelligence (AI) in the context of disaster management due to climate monitoring, riskanalytics, and early warning systems. The use of AI-enabled technologies such as machine learning (ML), deeplearning (DL), and predictive analytics to keep track of the environmental changes, predict extreme weatherpatterns, and estimate the risks related to climate change is gaining more and more adherents. This paper discusseshow AI tools can be used to enhance the precision of disaster forecasting, data processing in real-time, and resourcedistribution to coastal populations. The findings reveal that AI-based models can offer lead time of up to 48 hoursduring the occurrence of disasters related to weather and increase the effectiveness of early warning and disasterpreparedness. Through the combination of AI and geospatial information and social-economic aspects, people livingin the coastal area will be able to make better decisions, allocate the resources more efficiently, and mitigate theconsequences of climate-related emergencies. Regardless of the encouraging outcomes, AI implementation isassociated with the problem of data quality, computing resources, and ethical issues, including bias and data privacy.The paper ends by mentioning the potential the AI has in the future to develop scalable climate resilience methodsand the necessity to carry out further research to address the current obstacles in AI implementation in disastermanagement.