Reshaping Disaster Resilience: The AI and Machine Learning Revolution in Natural Catastrophe Management

Reshaping Disaster Resilience: The AI and Machine Learning Revolution in Natural Catastrophe Management

R. Siva Subramanian, D. Prabha, S. Srinivasan, S. Thirumurugaveerakumar, G. Gokilakrishnan
Copyright: © 2024 |Pages: 23
DOI: 10.4018/979-8-3693-2280-2.ch002
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Abstract

Natural disasters, such as earthquakes, hurricanes, floods, and wildfires, are ongoing worldwide concerns that have catastrophic effects on human lives, infrastructure, and the environment. By enhancing forecast, management, and reaction tactics, machine learning and artificial intelligence (AI) have emerged as revolutionary technologies in solving these crises. This comprehensive chapter delves deeply into the uses and implications of machine learning and AI in natural disaster avoidance. Machine learning techniques, especially artificial neural networks (ANNs), have shown promise in forecasting the incidence and severity of many natural catastrophes. These models make use of massive datasets including climatic, geographical, and historical data to improve forecasting accuracy and early warning systems. Furthermore, data-driven insights enable catastrophe prediction and risk assessment using a variety of machine learning methods ranging from decision trees to deep learning networks.
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Introduction

One of the most scary and catastrophic things that can happen to people is a natural catastrophe (Johns A 2013). They attack with a ruthless quickness that leaves devastation in their path and are absolutely unexpected. In addition to having an effect on our life, they can damage important infrastructure and upset the delicate balance of our ecosystem. There is an urgent need for innovative ways to foresee, manage, and react to catastrophic calamities in light of these enormous and sometimes apparently insurmountable problems (Razali et al 2020). AI and ML have become rays of hope in recent years despite the turmoil caused by natural catastrophes. These remarkable technologies provide fresh opportunities to comprehend these catastrophes better and, more crucially, lessen their devasting repercussions (Sun et al 2020). This review explores at artificial intelligence (AI) and machine learning, two fields that might fundamentally alter how we respond to disasters. These innovations have the potential to dramatically enhance our capacity for catastrophe prediction, real-time monitoring, and resource allocation. AI and machine learning provides us optimism that we may save lives and reduce the financial losses that often accompany these catastrophes by using the power of data and analytics (Goswami et al 2018). The importance of AI and ML for controlling, forecasting, and reacting to catastrophes cannot be overstated. By providing answers to issues that have plagued society for ages, they have the potential to dramatically alter how we deal with these calamities. The goal of this review paper is to provide a thorough overview of the developments in AI and machine learning technologies, their real-world applications, and the difficulties that come with catastrophe management. In addition, will outline creative alternatives and explore about how they can alter catastrophe planning and response in the future.

Objectives

  • 1.

    To provide a thorough investigation of the AI & ML applications in the context of disaster management & response. 2. Investigate and contrast various machine learning and AI approaches used in disaster management, highlighting their individual advantages, disadvantages, and applicability for various natural disaster kinds and situations. 3. Look into and deal with the typical issues and difficulties that come up during natural catastrophes, such as communication problems, resource limitations, and unanticipated circumstances. Examine the use of AI and machine learning to address these issues. 4. Examine the precise contribution that deep learning technology can make to improving recovery and response to disasters. Discuss about its uses in damage assessment, anomaly identification, and image analysis. 5. Encourage those working in research, policy, and practice to investigate and develop the possibilities of AI and machine learning in disaster management. Promote the development of innovative techniques and technology to better shield populations from the effects of natural catastrophes.

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