Natural disasters ranging from earthquakes to wildfires and floods pose serious threats to human life and infrastructure worldwide. As the frequency and severity of such events increase, new innovative solutions are necessary to ensure disaster preparedness, response, and recovery efforts and powerful prevention tools. This abstract provides an overview of the importance of machine learning in the management of natural disasters using machine learning. It also facilitates quick analysis of critical factors such as weather, soil type, demographics, and infrastructure vulnerabilities, contributing to more effective decision-making for disaster management and recovery efforts. This explores applications of machine learning in disaster scenarios, highlighting its versatility and potential impact. Machine learning can be used for image analysis and remote sensing of wildfire detection, flood forecasting, and damage assessment after earthquakes. Hence, ultimately saving lives and reducing the social and economic impact of these disasters.
TopIntroduction
Disaster management is a critical discipline dedicated to mitigating the impact of natural and human-made catastrophes, encompassing preparedness, response, recovery, and mitigation strategies. In the face of increasing global challenges posed by natural disasters, such as earthquakes, floods, hurricanes, and human-induced crises like pandemics and industrial accidents, the need for robust and adaptive disaster management approaches has become paramount. Traditionally, disaster management strategies have relied on human expertise, historical data analysis, and predefined protocols to handle emergencies. However, the limitations of these traditional methods in dealing with the complexities and unpredictability of disasters have prompted a search for innovative solutions. This quest for innovation has led to the integration of cutting-edge technologies, notably machine learning, into the domain of disaster management (Adger & Brooks, 2003; Alexander, 2002a; Dger et al., 2001; O’Brien, 2006; Kathleen Geale, 2012; Lettieri et al., 2009).
Machine learning, a subset of artificial intelligence, has emerged as a transformative force, enabling systems to learn from data, identify patterns, and make predictions or decisions without explicit programming. Its capacity to handle vast amounts of diverse and dynamic data has rendered machine learning invaluable in addressing the multifaceted challenges inherent in disaster scenarios.
The significance of leveraging machine learning in disaster management lies in its potential to revolutionize various aspects of the field. From enhancing early warning systems to optimizing resource allocation, from predicting disaster occurrences to aiding in real-time response efforts, machine learning offers a paradigm shift in improving the efficacy and efficiency of disaster management strategies.
Within this context, this paper aims to explore and delve deeper into the intersection of machine learning and disaster management. Specifically, the paper will examine the existing landscape of machine-learning applications in disaster scenarios and focus on a particular machine-learning technique or algorithm. Through a comprehensive review and analysis, the paper aims to elucidate the strengths, limitations, and potential applications of this chosen technique within the realm of disaster management.
The subsequent sections will provide a detailed exploration of the literature surrounding disaster management practices, the role of technology in reshaping these practices, and a critical review of machine learning algorithms applied in disaster management scenarios (Aguirre, 2020; Dessai et al., 2001; DFID (Department for International Development), 2004a; Dilley et al., 2005; EM-DAT (Emergencies Disasters Data Base), 2005; Larsen, 2003). Additionally, this paper will spotlight a specific machine learning technique, detailing its workings, its relevance to disaster management, and any proposed innovations aimed at advancing its applicability in mitigating disasters' impact.
In synthesizing existing knowledge and proposing innovative approaches, this paper endeavors to contribute to the ongoing discourse on leveraging machine learning to fortify disaster management strategies, thereby fostering more resilient and adaptive responses to future disasters.