Global warming worsens natural disasters and humanitarian issues. Disaster prediction relies on satellites and weather stations. AI may help catastrophe management. AI reduces disaster risk in many ways. Early warning systems, weather forecasts, disaster recovery, and reconstruction improve. AI could help us predict, prepare, and recover from natural calamities. These technologies provide climate change mitigation and community protection hope. They propose a better future amid climate change catastrophes. DRR is aggressively adopting AI, notably ML. This field encompasses severe event prediction, hazard mapping, real-time detection, situational awareness, decision assistance, and more. Growing usage of AI in disaster management raises questions about its benefits. We face what issues? How can these difficulties be resolved and opportunities maximised? What can AI tell policymakers, stakeholders, and the public to reduce disasters? The chapter introduces catastrophe management AI, AI implementation issues, and solutions to make the world more peaceful will be addressed.
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The utilization of advanced technology has become imperative for effective disaster risk management in a time characterized by the increasing occurrence and intensity of natural disasters. Artificial intelligence (AI) is currently at the forefront of innovative approaches, offering the potential for a fundamental shift in our methods of predicting, responding to, and recovering from catastrophic events. Artificial intelligence (AI) solutions, encompassing various techniques such as machine learning algorithms and big data analytics, present a unique opportunity to enhance the effectiveness and precision of disaster management activities. This comprehensive study delves into the fundamental necessity of incorporating artificial intelligence (AI) technology into systems for managing catastrophic risks. Natural disasters can disrupt the regular process of data collection and reporting, highlighting the necessity for innovative techniques. This study examines the significant contribution of artificial intelligence (AI) in addressing the aforementioned issue. Specifically, our focus is on the utilization of AI in several domains like the Internet of Things (IoT), edge computing, remote sensing, social media analysis, big data utilization, and the interplay between data quality and AI.
The discourse commences by centering on the Internet of Things (IoT), emphasizing its capacity to provide instantaneous data, a crucial aspect for expeditious decision-making in times of calamities. The concept of edge computing is now under investigation due to its potential to offer localized processing capabilities, particularly in areas characterized by unreliable network connectivity. The advancement of remote sensing technology has emerged as a valuable means of obtaining essential data, while social media platforms offer timely views from affected communities. The narrative subsequently transitions to the transformative impact of big data, highlighting its capacity to enhance disaster response through the analysis of vast amounts of information. The significance of data quality is underscored, underscoring the necessity of accurate information for making well-informed decisions. Cloud computing has emerged as a facilitator, enabling enhanced accessibility to computer resources and data storage, hence enhancing organizational responsiveness.
This study explores the intricate relationship between data and artificial intelligence (AI), elucidating the significant reliance of AI models on data for both training purposes and the enhancement of their accuracy. Deep learning and reinforcement learning are two machine learning algorithms that are now being studied for their potential applications in disaster response, predictive modeling of crop yielding (Ali Shaik M., Geetha M., et al., 2022), and decision support. However, the potential outlook of artificial intelligence in the domain of catastrophic risk management is not devoid of challenges. The ensuing discourse examines the constraints and limitations associated with the application of artificial intelligence (AI), encompassing factors such as the quality and quantity of data, privacy and security considerations, computer resources, the intricacies of integration, real-time performance, potential biases, and ethical considerations.
Notwithstanding these challenges, the narrative underscores the transformative capacity of artificial intelligence in the realm of catastrophe management. Artificial intelligence (AI) offers a multitude of benefits, encompassing early warning systems, automated damage assessment, and personalized response strategies. This study examines the impact of artificial intelligence (AI) on several aspects of resource allocation, cooperation, scalability, cost-effectiveness, and ongoing evaluation and assessment. The debate continues by emphasizing the need to raise public awareness and support AI deployments in catastrophic risk management. Although there are still challenges to overcome, it is clear that AI technology can boost disaster prediction, response, and recovery efficiency and accuracy.