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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
AI and ML in Early Warning Systems for Natural Disasters bridges the gap between advanced computational models and real-world disaster management practices by highlighting how data-driven intelligence can enhance resilience planning and reduce risks in the face of climate change and extreme environmental events. Beginning with an overview of traditional early warning systems and the limitations they face in accuracy and timeliness The book sheds light on to AI- and ML-driven approaches, detailing predictive analytics, anomaly detection, sensor networks, geospatial data integration, and IoT-enabled monitoring systems. Case studies on earthquake prediction, flood forecasting, cyclone tracking, and wildfire detection illustrate the practical applicability of AI-powered models across diverse contexts. Later chapters examine legal frameworks, ethical considerations, and community-based strategies that ensure responsible, sustainable, and inclusive deployment of these technologies. Key Features Presents AI and ML techniques for predictive analytics, anomaly detection, and risk modeling in disaster scenarios. Demonstrates real-world applications through case studies on earthquakes, floods, cyclones, and wildfires. Explores integration of satellite imagery, remote sensing, and IoT-based sensor networks for real-time monitoring. Assesses legal, regulatory, and ethical frameworks shaping AI use in disaster preparedness. Provides multidisciplinary insights, blending computer science, engineering, and disaster management for resilient community planning.
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Veröffentlichungsjahr: 2025
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FOREWORD
The book AI and ML in Early Warning Systems for Natural Disasters is a timely publication by distinguished scholars in the field. Its twelve chapters are contributed by renowned experts in AI and ML, disaster science and management, extreme climate events, and early warning systems. Early warning is a critical phase of disaster management, with investments proving to be over ten times more cost-effective in reducing deaths and losses caused by disasters. This book comprehensively addresses various aspects of early warning systems for disaster management, focusing on the most pressing natural hazards facing the world today. This book will be an indispensable resource for students, researchers, educators, and practitioners eager to explore the transformative role of AI and ML in disaster preparedness and response. It will also serve as a vital guide for organizations and agencies working at the forefront of disaster risk reduction, offering insights into innovative strategies that can be scaled and adapted globally. In an era where the stakes of inaction are higher than ever, this book stands as a critical contribution to building a safer, more resilient world.
FOREWORD II
In an era marked by escalating climate crises and the increasing frequency of natural disasters, the need for innovative solutions has never been more urgent. The book AI and ML in Early Warning Systems for Natural Disasters arrives at a critical juncture, offering a transformative perspective on the use of cutting-edge technology to address one of humanity’s most pressing challenges.
Early warning systems are a cornerstone of disaster risk reduction, providing invaluable time to prepare and respond, thereby saving lives and reducing economic losses. However, the traditional approaches to early warning often struggle to keep pace with the complexities of modern disasters, characterized by rapid onset, evolving patterns, and compounding effects. This is where Artificial Intelligence (AI) and Machine Learning (ML) can revolutionize the field.
This book, meticulously compiled by some of the most distinguished minds in the disciplines of AI, ML, and disaster management, bridges the gap between technological advancements and practical applications in disaster risk reduction. It provides an in-depth exploration of how AI and ML can enhance predictive accuracy, optimize data processing, and deliver timely insights to improve preparedness and response.
The twelve chapters in this book address diverse aspects of early warning systems, from forecasting extreme climate events and monitoring geological hazards to integrating ethical frameworks and ensuring equitable access to technology. Importantly, it highlights the potential of AI and ML to support vulnerable populations and improve decision-making in resource-constrained environments, demonstrating a commitment to inclusive and sustainable development.
As a scholar and practitioner deeply engaged in disaster risk reduction, I am heartened by the emphasis on interdisciplinary collaboration presented in this volume. The integration of AI and ML with traditional knowledge, policy frameworks, and community-based approaches represents a holistic and forward-thinking strategy for mitigating disaster risks.
This book is not only a testament to the remarkable progress we have made in technological innovation but also a clarion call for action. It challenges researchers, practitioners, and policymakers to harness the power of AI and ML responsibly, ensuring that these technologies serve as tools for resilience and empowerment.
I am confident that this book will serve as an indispensable resource for academics, professionals, and organizations striving to create a safer, more resilient world. It is a vital contribution to the global discourse on disaster risk reduction and an inspiring roadmap for the integration of technology into one of humanity’s most critical endeavors.
PREFACE
In an era marked by escalating natural disasters, the integration of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) has emerged as a transformative force in disaster management. The increasing frequency and intensity of calamities such as floods, earthquakes, and wildfires demand innovative solutions that go beyond traditional methods of detection and mitigation. This compilation of chapters explores the critical role of AI and ML in addressing these challenges, focusing on their potential to revolutionize disaster detection, management, and prevention. By delving into cutting-edge research, tools, and methodologies, the book aims to provide a comprehensive understanding of how technology is shaping the future of disaster resilience.
The first few chapters underscore the importance of AI and ML in disaster detection, offering a foundational perspective on their transformative capabilities. Traditional approaches often struggle to provide accurate, real-time data, whereas AI-driven models leverage large datasets, remote sensing, and predictive analytics to improve accuracy and timeliness. By examining the evolution of these technologies, readers will gain insights into their ability to anticipate disasters and reduce human and economic losses significantly.
Moving forward, the text explores recent advances in AI and ML techniques, emphasizing innovative applications across various natural disasters. From leveraging satellite imagery and IoT-based sensors for real-time monitoring to deploying sophisticated machine learning algorithms for pattern recognition, these chapters showcase the dynamic interplay between technology and disaster management. Real-world case studies further illustrate how these advancements are being implemented to save lives and protect communities worldwide.
The book also delves into the integration of AI and ML into early warning systems, a critical component of modern disaster preparedness. These systems not only enhance the predictive accuracy of traditional methods but also enable more effective communication and coordination among stakeholders. A dedicated section examines the challenges of implementing such systems in the context of climate change, highlighting the urgent need for scalable and adaptive solutions.
Finally, the book addresses the broader implications of AI and ML in disaster management, including legal frameworks and ethical considerations. With technology advancing at an unprecedented pace, ensuring responsible development and deployment is paramount. Additionally, specialized chapters focus on unique topics such as the use of fuzzy artificial intelligence for earthquake prediction and the potential of these technologies to mitigate the long-term impacts of climate change.
By presenting a holistic view of the field, this book aims to inspire researchers, policymakers, and practitioners to harness the full potential of AI and ML for disaster management. The insights and strategies offered within these pages underscore the transformative power of technology, emphasizing its critical role in creating a safer, more resilient world.
List of Contributors
Importance of Artificial Intelligence and Machine Learning in Disaster Detection
Mustapha Ismail Kwari1,*,N. Rajkumar1,Vinoth Kumar1Abstract
The rapid progress of Artificial Intelligence (AI) and Machine Learning (ML) has raised hopes that these technologies could transform the way we detect and manage disasters. Nevertheless, this chapter contends that the current abilities of AI and ML are exaggerated and face inherent constraints in effectively identifying and responding to the intricacies inherent in disasters. AI and ML systems, despite their computational power, struggle to understand the complex nature of disasters due to their reliance on historical data and susceptibility to biases and noise in training data, potentially causing inaccurate predictions and worsening disaster impacts. Moreover, AI and ML currently lack contextual understanding and adaptability for real-world crises, requiring human judgement, intuition, and improvisation to navigate dynamic environments. This chapter delves into the ethical and societal consequences of relying too heavily on AI and ML for disaster detection and management. It highlights the dangers of perpetuating biases, compromising privacy and accountability, and potentially causing harm through flawed decision-making processes. The chapter also stresses the importance of human oversight, interdisciplinary collaboration, and a holistic approach that integrates AI and ML capabilities with local knowledge, robust emergency response plans, and effective communication strategies. The chapter highlights the limitations of AI and ML in disaster detection, advocating for a balanced approach that balances their strengths while acknowledging their limitations. Recognizing the complexities of disasters enables policymakers and disaster management professionals to make informed decisions and develop more resilient strategies for mitigating and responding to these critical events.
INTRODUCTION
Natural and man-made crises upset an organization's stability, goals, and operations. It can cause tension and emotional reactions, upend the existing quo, and delegitimize policies. Crises, however, can speed up the political learning and transformation processes and provide public organisations with an opportunity to learn how government institutions function under duress. These situations could put people under stress and jeopardise an organization's capacity to continue operating [1].
Catastrophes and emergencies are unpredictable occurrences that have substantial effects on the environment and society. Their intricately linked social amenities and ecological contexts make it difficult to forecast their path and mitigate their adverse effects [2, 3]. Global warming and environmental pollution increase natural disasters, particularly in developing nations. Studying these disasters using raw smartphone data and anomaly detection algorithms can improve community and catastrophe management [4]. There is an immediate need for strategic disaster risk management (DRM), and artificial intelligence could enhance decision-making. Disasters are now a bigger global concern due to factors like climate change, urbanisation, population growth, and environmental degradation. These incidents result in fatalities, economic upheaval, and long-term system and infrastructure damage [5]. Fig. (1) shows factors contributing to global disaster.
The advancements in artificial intelligence (AI) and machine learning (ML) have improved disaster detection by analysing data from various sources, enabling accurate prediction and management of events, thereby reducing damage to infrastructure and human life from natural and artificial disasters [6].
Fig. (1)) Factors contributing to global disaster.According to the United Nations Office for Disaster Risk Reduction (UNISDR), a disaster is an important disturbance to a community's normal operations that results in severe losses to people, property, the economy, or the environment that are greater than what the community is able to handle [7, 8].
Disaster Risk Management (DRM) involves assessing and mitigating risks from crises and disasters. Accurate information is essential, and stakeholders must work together to achieve it. The increasing volume of data being used from several sources, including social media and the Internet of Things, presents opportunities to employ AI and ML to enhance DRM decision-making [5].
The goals of disaster management operations are to minimise casualties, safeguard people and property, lessen the effects on the economy, and restore normalcy. They are carried out before, during, and afterwards. Disaster management requires robust decision-making due to the complexity of catastrophes and the criticality of operations, with AI and ML advancements enabling informed and effective management [8, 9]. Disaster management involves the systematic management of disaster prevention, preparedness, response, and recovery, with four stages: preparedness, response, mitigation, and recovery [10]. While preparedness and response involve preparing the community for emergency planning, mitigation concentrates on preventing or lessening the effects of disasters, and recovery is taking long-term measures to return things to normal. Resilience can be promoted by local communities' active participation in catastrophe management. Success or failure in disaster management depends on the use of effective practices. Resilience in emergency aid can be improved by utilising AI and GIS technologies. Planning for disaster response is impacted by morphology, weather, ecology, and resource availability, among other things. Disaster management relies heavily on preparedness, resilience, vulnerability, and preventive efforts to lessen the effects of disaster [8, 9, 11].
The Centre for Research on the Epidemiology of Disasters reports that the United States, China, Japan, and India have the highest GDP losses due to disasters, with the Asia-Pacific region being the most vulnerable since 1995 [12]. Real-time earthquake early warning systems are vital due to the global threat posed by seismic hotspots. Conventional models could result in more expenses and false alarms. The use of specialised instruments is lessened in major cities when people acquire smartphones, and mobile sensors provide better spatial resolution [4]. Fig. (2) illustrates the various stages involved in disaster management.
Artificial Intelligence
The idea of artificial intelligence was first proposed in the 1930s. They year 1950 and the 1956 meeting at Dartmouth College, where it was formally introduced, are credited with establishing AI as a scientific approach. AI simulates intelligent human behaviour using technology. The multidisciplinary field of AI combines computer science, logic, biology, psychology, and philosophy to increase productivity, reduce labour costs, maximise human resources, and provide job opportunities [13]. The term AI refers to “computers or machines that replicate cognitive processes, like learning and problem-solving, that individuals recognise with the human mind” [10]. AI is a branch of computer science that trains machines to do things that are impossible for humans to accomplish. These machines frequently make decisions based on patterns found in large training datasets [14, 15]. Another classification of AI levels consists of the following (a) Artificial narrow intelligence, which comprises all of the AI that exists today; (b) Artificial general intelligence, which emphasises the idea that AI agents can learn, perceive, understand, and behave exactly like humans; and (c) Artificial superintelligence, which seeks to replicate the diverse aspects of human intelligence and surpass it in every way [16, 17]. Fig. (3) presents classification levels of artificial intelligence.
Fig. (2)) Stages of disaster management.AI is being used in a variety of domains, most notably disaster management, to improve forecast performance, allow for prompt mitigation, and lessen property and human damage [18]. AI enhances disaster risk management by prioritizing work, allocating resources, evaluating post-disaster damage, identifying infrastructure concerns, and offering long-term recovery solutions. It supports Sustainable Cities and Communities objectives by enhancing resilience [5]. AI applications have significantly influenced research on how societies respond to risks and disasters. Among these applications are robotics, drone technology, tracking, mapping, geospatial analysis, remote sensing, ML, and more. Social science researchers have used a variety of techniques and strategies to study risks and calamities. In several areas, AI is predicted to surpass humans in the next ten years [19]. AI has the ability to substantially lessen the workload of decision-makers in handling catastrophes by facilitating the process of evaluating immense quantities of data attributed to disasters [9].
Fig. (3)) Classification of artificial intelligence levels.Natural disasters, including hurricanes, floods, fires, and earthquakes, have resulted in substantial damage and monetary losses, necessitating management duties like recovery, intervention, and rescue. Real-time updates are provided by social networks and data analysis and detection are automated by AI. Twitter is a popular tool for text-based, visual content analysis, and satellite image technologies used in disaster detection [20].
With applications in prediction, change detection, early warning systems (EWS), vulnerability management, spatial modelling, and mitigation strategies, AI has significantly advanced geohazard modelling. Scientific decision-making on geohazards has been facilitated by the abundance of data from remote sensing, meteorology, and studies [19]. AI is a potent instrument for pattern recognition, task optimisation, and large-scale data analysis. It is employed to capture high-level abstractions and improve ML, a branch of AI that employs complex statistical techniques to gradually improve jobs [4]. AI can completely transform crisis management through data analysis, early warning detection, real-time monitoring, and the development of detailed plans. AI-driven virtual assistants and chatbots support decision-making, resource allocation, and response optimisation. But human judgement and experience remain indispensable [1].
Machine Learning
ML is the term used to describe computer algorithms that can automatically learn from data in the context of AI [16, 21]. ML is a branch of AI that focuses on modelling techniques that let a computer learn from data and prior knowledge [10]. The application of AI that allows computer systems to automatically learn from experience without the need for explicit programming is known as ML. AI is the capacity of machines to imitate intelligent human behaviours [14].
Large and complicated datasets can be used with the help of ML and deep learning to create systems that can anticipate disasters, aid in their reaction and recovery, and produce useful decision-support tools. Through the manipulation of data kinds from multiple sources, these strategies can identify patterns that can yield intelligence that would otherwise be impossible to reveal [8]. The datasets most frequently utilised in XAI-DRM investigations are those from remote sensing (satellite, SAR, drone photos) and earth science (geology, geomorphology, rainfall). To a lesser degree, additional data sources like traffic, social media, GIS, IoT, socioeconomic, climatic, and weather data, as well as simulated data, are also utilised. In order to determine the optimal soil erodibility indices, earth data including geological, geomorphological, and rainfall data were processed using ML techniques. Data on the climate and weather, traffic, social media (such as tweets), geographic information systems (GIS), Internet of Things (IoT), socioeconomic data (such as demographic data), and simulated data [5]. Fig. (4) illustrates common data sources used in disaster detection.
Fig. (4)) Common data sources in disaster detection.The primary benefits of ML algorithms lie in their high level of automation as well as their ease of use in identifying patterns and trends within datasets. Furthermore, multi-dimensional and multi-variety data can be used with ML techniques [22]. ML techniques have several benefits, such as being quick, affordable, effective, and simple to validate [23]. ML, a branch of AI, makes classifications and predictions from historical or present data. It covers supervised, unsupervised, semi-supervised as well as reinforcement learning. Under supervised learning, unknown functions such as regression and classification that link the input and output variables are found. A single input variable is used in unsupervised learning, where the model searches for structures and patterns like association and clustering; many input variables but few output variables are used in semi-supervised learning; and the best course of action is identified in reinforcement learning [10, 15, 19, 21, 24]. Fig. (5) highlights various categories of machine learning algorithms.
Fig. (5)) An overview of categories of ML algorithms.Ali and Ahmad illustrated the potential of AI and ML in crisis management by measuring, recognising, and prioritising dangers. These instruments enable proactive risk management techniques and uncover hidden relationships [1]. Ghaffarian et al. focused on applying ML algorithms to satellite imagery in order to forecast wildfires, allowing for early warning systems and timely actions to protect affected areas [5]. Dikshit et al. demonstrated the potential of ML in geohazard assessments by utilising the abundance of meteorological, remote sensing, and ground-based data [19].
The application domains include, among other natural disasters, earthquakes, floods, wildfires, hurricanes, and landslides. Recent advances in technology can also be beneficial in managing man-made disasters like refugee crises [8, 9, 11]. Common natural disasters and how AI and ML are being used for their detection and management include;
AI and Ml in Earthquake Detection
An earthquake is an abrupt movement of the Earth's crust brought on by the violent motions under the surface of the Earth induced by volcanic activity, with disastrous results [25]. In numerous regions of the world, earthquakes occur frequently. Iran, Taiwan, south California, Japan, Indonesia, and Turkey are the areas most vulnerable to earthquakes. If an earthquake's magnitude is greater than 2.5, people can feel it; if it is less than 2.5, they won't. Earthquakes with significant damage had magnitudes greater than 4.5. Large numbers of fatalities can occasionally be attributed to earthquakes [26]. Earthquakes are deadly catastrophic natural events that necessitate knowledge of their physical characteristics and interactions [27]. The intricate structure of Earth is studied by seismology aficionados who use methods such as migration, deconvolution, and filtering to analyse data, find subsurface patterns, and cut out undesired frequencies [28]. While early studies concentrate on anomaly finding, remote sensing technology is helpful in seismic research. Satellite remote sensing monitors changes in the long-range thermal field, which helps to minimise damage [27]. The difficulty of accurately predicting earthquakes stems from the absence of distinct patterns in seismic data. Short-term earthquake predictions are useful for evacuation planning, whereas long-term earthquake predictions can be assisted by AI techniques. The periodic arrival of earthquakes, which can aid in establishing guidelines for construction codes and disaster response strategies, is the basis for long-term forecasts. An earthquake of 5.9 magnitude struck the Italian city of L'Aquila in 2009, resulting in extensive destruction of infrastructure and a mass slaughter. Precursor-based earthquake studies about the stresses and strains on the earth should be studied from a variety of sites and sensors, according to the International Association of Seismology and Physics of the Earth's Interior (IASPEI) [29]. Earthquakes are being identified using MEMS sensors and IoT technology; the MyShake project is the first global system of its kind, and accurate identification requires effective machine learning [29].
