This book promotes a meaningful and appropriate dialogue and cross-disciplinary partnerships on Artificial Intelligence (AI) in governance and disaster management. The frequency and the cost of losses and damages due to disasters are rising every year. From wildfires to tsunamis, drought to hurricanes, floods to landslides combined with chemical, nuclear and biological disasters of epidemic proportions has increased human vulnerability and ecosystem sustainability. Life is not as it used to be and governance to manage disasters cannot be a business as usual. The quantum and proportion of responsibilities with the emergency services has increased many times to strain them beyond their human capacities. Its time that the struggling disaster management services get supported and facilitated by new technology of combining Artificial Intelligence (AI) and Machine Learning (ML) with Data Analytics Technologies (DAT)to serve people and government in disaster management. AI and ML have advanced to a state where they could be utilized for many operations in disaster risk reduction. Even though many disasters cannot be prevented and a number of them are blind natural disasters yet through an appropriate application of AI and ML quick predictions, vulnerability identification and classification of relief and rescue operations could be achieved.

Human life is at risk because of human actions and natural disasters. Natural disasters are imminent, and it is almost difficult to recover completely from the damage caused. Technology has become a solution to many longstanding issues, and while current technologies may be successful, they are far from completely solving the nuanced, complex, challenging, and daunting tasks connected with disaster missions and risky interventions. It is not enough to use ICT only in the process of disaster management, as the current and future period of innovation is Artificial Intelligence. It is also important to use artificial intelligence in the process of disaster management to reduce the loss of human life and to save time by using robots, drones, sensors, etc.Modern search and rescue workers are equipped with a powerful toolkit to address natural and man-made disasters. The first chapter, in this book, presents an introductory overview of how a new tool can be added to this toolkit: robots. The use of robotic assets in search and rescue operations is explained and an overview is given of the worldwide efforts to incorporate robotic tools in search and rescue operations. The second chapter categorizes the source of disasters and associated missions and highlights the needs for suitable and reliable technology and technical and functional requirements of robotic systems to fulfill task objectives. Additionally, the book presents up-to-date coverage on unmanned aerial systems; UAV- based photogrammetry and recomputing for hazards and disaster risk monitoring; unmanned ground robots for rescue tasks; crisis analytics: big data-driven crisis response; towards gas discrimination and mapping in emergency response scenarios using a mobile robot with an electronic nose; a survey on robot-assisted crowd evacuation under emergency situations; improved Bayesian network-based risk model and its application in disaster risk assessment; command and control systems for search and rescue robots; and overcoming data scarcity in flood hazard assessment using remote sensing and artificial neural network. Finally, the book describes how the different ICARUS unmanned search and rescue tools have been evaluated and validated using operational benchmarking techniques.

A comprehensive, authoritative, and accessible reference for disaster robotics that covers theory, specific deployments, and ground, air, and marine modalities. This book offers the definitive guide to the theory and practice of disaster robotics. It can serve as an introduction for researchers and technologists, a reference for emergency managers, and a textbook in field robotics. Written by a pioneering researcher in the field who has herself participated in fifteen deployments of robots in disaster response and recovery, the book covers theory and practice, the history of the field, and specific missions. After a broad overview of rescue robotics in the context of emergency informatics, the book provides a chronological summary and formal analysis of the thirty-four documented deployments of robots to disasters that include the 2001 collapse of the World Trade Center, Hurricane Katrina, the 2010 Haiti earthquake, the Deepwater Horizon oil spill, the 2011 Japanese earthquake and tsunami, and numerous mining accidents. It then examines disaster robotics in the typical robot modalities of ground, air, and marine, addressing such topics as robot types, missions and tasks, and selection heuristics for each modality. Finally, the book discusses types of fieldwork, providing practical advice on matters that include collecting data and collaborating with emergency professionals. The field of disaster robotics has lacked a comprehensive overview. This book by a leader in the field, offering a unique combination of the theoretical and the practical, fills the gap.

​This handbook is a comprehensive source of information, analysis and directions in disaster studies. It goes beyond the oft-explored issues of management and science related to the topic and explores policies, governance, law and decision-making combined with the processes of implementation and enforcement, all the while integrating the latest science and technology updates related to the topic, such as artificial intelligence and early warning systems. It brings together studies which relate to sociology, politics and institutional economics, which work under the impact of resource availability, issues of leadership and international laws. Disasters are trans-boundary and disaster studies are trans-disciplinary. It is this aspect which would form the fulcrum of contributions and present a new, refreshing and innovative design for the handbook. The transformatory pedagogy which started with the Hyogo Framework for action 2005-2015 and The Sendai Framework for Disaster Risk Reduction 2015-2030 outlines seven clear targets and four priorities for action to prevent new and reduce existing disaster risks. The four priority areas around which the book would revolve are (i) Understanding disaster risk; (ii) Strengthening disaster risk governance to manage disaster risk; (iii) Investing in disaster reduction for resilience and; (iv) Enhancing disaster preparedness for effective response, and to "Build Back Better" in recovery, rehabilitation and reconstruction.

In a world where natural disasters wreak havoc with increasing frequency and severity, the need for accurate prediction and effective management has never been more critical. From earthquakes shattering communities to floods submerging vast regions, these events endanger lives and strain resources and infrastructure to their limits. Yet, amidst this turmoil, traditional forecasting methods often need to catch up, leaving us vulnerable and reactive rather than proactive. This comprehensive academic collection provides a beacon of hope in uncertain circumstances: Internet of Things and AI for Natural Disaster Management and Prediction. By bridging the gap between theory and practice, this book empowers academics, policymakers, and practitioners alike to harness the full potential of machine learning in safeguarding lives and livelihoods.

Acute events of natural origin, spanning atmospheric, biological, geophysical, hydrologic, and oceanographic realms, persistently menace societies globally. Approximately 160 million people annually bear the brunt of these disasters, with certain regions facing disproportionate impacts. The lack of predictability intensifies the challenge, creating intercommunal capacity gaps and amplifying the dire consequences. Utilizing AI and Machine Learning for Natural Disaster Management provides instances of ML in predicting earthquakes. By leveraging seismic data, AI systems can analyze magnitude and patterns, providing invaluable insights to forecast earthquake occurrences and aftershocks. Similarly, the book unveils the potential of ML in simulating floods by recording and analyzing rainfall patterns from previous years. The predictive power extends to hurricanes, where data on wind speed, rainfall, temperature, and moisture converge to anticipate future occurrences, potentially saving millions in property damage.

A comprehensive survey of artificial intelligence algorithms and programming organization for robot systems, combining theoretical rigor and practical applications. This textbook offers a comprehensive survey of artificial intelligence (AI) algorithms and programming organization for robot systems. Readers who master the topics covered will be able to design and evaluate an artificially intelligent robot for applications involving sensing, acting, planning, and learning. A background in AI is not required; the book introduces key AI topics from all AI subdisciplines throughout the book and explains how they contribute to autonomous capabilities. This second edition is a major expansion and reorganization of the first edition, reflecting the dramatic advances made in AI over the past fifteen years. An introductory overview provides a framework for thinking about AI for robotics, distinguishing between the fundamentally different design paradigms of automation and autonomy. The book then discusses the reactive functionality of sensing and acting in AI robotics; introduces the deliberative functions most often associated with intelligence and the capability of autonomous initiative; surveys multi-robot systems and (in a new chapter) human-robot interaction; and offers a “metaview” of how to design and evaluate autonomous systems and the ethical considerations in doing so. New material covers locomotion, simultaneous localization and mapping, human-robot interaction, machine learning, and ethics. Each chapter includes exercises, and many chapters provide case studies. Endnotes point to additional reading, highlight advanced topics, and offer robot trivia.