Utilizing Ai And Machine Learning For Natural Disaster Management
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| Author | : Satishkumar, D. |
| Publisher | : IGI Global |
| Total Pages | : 374 |
| Release | : 2024-04-29 |
| Genre | : Nature |
| ISBN | : |
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.
| Author | : Satishkumar, D. |
| Publisher | : IGI Global |
| Total Pages | : 378 |
| Release | : 2024-03-07 |
| Genre | : Nature |
| ISBN | : |
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.
| Author | : Satishkumar, D. |
| Publisher | : IGI Global |
| Total Pages | : 360 |
| Release | : 2024-02-16 |
| Genre | : Nature |
| ISBN | : |
In a world where the relentless force of natural and man-made disasters threatens societies, the need for effective disaster management has never been more critical. Predicting Natural Disasters With AI and Machine Learning addresses the challenges of disasters and charts a path toward proactive solutions by applying artificial intelligence (AI) and machine learning (ML). This book begins by interpreting the nature of disasters, clearly distinguishing between natural and man-made hazards. It delves into the intricacies of disaster risk reduction (DRR), emphasizing the human contribution to most disasters. Recognizing the necessity for a multifaceted approach, the book advocates the four ‘R’s - Risk Mitigation, Response Readiness, Response Execution, and Recovery - as integral components of comprehensive disaster management. This book explores various AI and ML applications designed to predict, manage, and mitigate the impact of natural disasters, focusing on natural language processing, and early warning systems. The contrast between weak AI, simulating human intelligence for specific tasks, and strong AI, capable of autonomous problem-solving, is thoroughly examined in the context of disaster management. Its chapters systematically address critical issues, including real-world data handling, challenges related to data accessibility, completeness, security, privacy, and ethical considerations.
| Author | : T. V. Vijay Kumar |
| Publisher | : Springer Nature |
| Total Pages | : 267 |
| Release | : 2020-10-12 |
| Genre | : Business & Economics |
| ISBN | : 9811542910 |
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.
| Author | : Ouaissa, Mariyam |
| Publisher | : IGI Global |
| Total Pages | : 317 |
| Release | : 2024-05-06 |
| Genre | : Computers |
| ISBN | : |
In our rapidly evolving digital landscape, the threat of natural disasters looms large, necessitating innovative solutions for effective disaster management. Integrating Artificial Intelligence (AI) and the Internet of Things (IoT) presents a transformative approach to addressing these challenges. However, despite the potential benefits, the field needs more comprehensive resources that explore the full extent of AI and IoT applications in disaster management. AI and IoT for Proactive Disaster Management fills that gap by examining how AI and IoT can revolutionize disaster preparedness, response, and recovery. It offers a deep dive into AI frameworks, IoT infrastructures, and the synergy of these technologies in predicting and managing natural disasters. Ideal for undergraduate and postgraduate students, academicians, research scholars, industry professionals, and technology enthusiasts, this book serves as a comprehensive guide to understanding the intersection of AI, IoT, and disaster management. By showcasing cutting-edge research and practical applications, this book equips readers with the knowledge and tools to harness AI and IoT for more efficient and effective disaster management strategies.
| Author | : Ali, Mohammed |
| Publisher | : IGI Global |
| Total Pages | : 280 |
| Release | : 2022-04-18 |
| Genre | : Technology & Engineering |
| ISBN | : 1799898172 |
The increasing use of innovative technologies by global businesses has sparked debate about their application in crisis resolution. Resolution tools can be used by global businesses to manage various types of crisis situations, such as natural disasters, information security issues, economic downturns, health crisis situations, and sustainability issues in education, among others. Further study and consideration of the uses of technology in the areas of crisis and change management and intra-company communication practice in the context of global business must be done to ensure successful and sustainable businesses. Future Role of Sustainable Innovative Technologies in Crisis Management raises awareness of the multifaceted field of new technology in crisis management that has resulted in a paradigm shift in the way contemporary industries and global businesses communicate and conduct their daily business operations. This book defines the scope of innovative technologies as the application of new technologies to support the resolution of various types of crisis situations to achieve regulatory compliance and improved risk management in an effective and automated manner. Covering topics such as sustainable business and disaster scenarios, this reference work is ideal for managers, entrepreneurs, researchers, academicians, scholars, practitioners, instructors, and students.
| Author | : Ravinesh C. Deo |
| Publisher | : Springer Nature |
| Total Pages | : 469 |
| Release | : 2020-07-29 |
| Genre | : Technology & Engineering |
| ISBN | : 9811557721 |
This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables. Predictive modelling is a consolidated discipline used to forewarn the possibility of natural hazards. In this book, experts from numerical weather forecast, meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making contribute towards an interdisciplinary framework to construct potent models for hazard risk mitigation. The book will help advance the state of knowledge of artificial intelligence in decision systems to aid disaster management and policy-making. This book can be a useful reference for graduate student, academics, practicing scientists and professionals of disaster management, artificial intelligence, and environmental sciences.
| Author | : Bruce W. Clements |
| Publisher | : Butterworth-Heinemann |
| Total Pages | : 526 |
| Release | : 2016-02-23 |
| Genre | : Political Science |
| ISBN | : 0128019891 |
Disasters and Public Health: Planning and Response, Second Edition, examines the critical intersection between emergency management and public health. It provides a succinct overview of the actions that may be taken before, during, and after a major public health emergency or disaster to reduce morbidity and mortality. Five all-new chapters at the beginning of the book describe how policy and law drive program structures and strategies leading to the establishment and maintenance of preparedness capabilities. New topics covered in this edition include disaster behavioral health, which is often the most expensive and longest-term recovery challenge in a public health emergency, and community resilience, a valuable resource upon which most emergency programs and responses depend. The balance of the book provides an in-depth review of preparedness, response, and recovery challenges for 15 public health threats. These chapters also provide lessons learned from responses to each threat, giving users a well-rounded introduction to public health preparedness and response that is rooted in experience and practice. Contains seven new chapters that cover law, vulnerable populations, behavioral health, community resilience, preparedness capabilities, emerging and re-emerging infectious diseases, and foodborne threats Provides clinical updates by new MD co-author Includes innovative preparedness approaches and lessons learned from current and historic public health and medical responses that enhance clarity and provide valuable examples to readers Presents increased international content and case studies for a global perspective on public health
| Author | : Juha I. Uitto |
| Publisher | : Springer |
| Total Pages | : 289 |
| Release | : 2015-11-05 |
| Genre | : Nature |
| ISBN | : 443155078X |
This book focuses on exploring the linkages between natural disasters and sustainable development at the global, regional, and national levels. Disasters and development are closely related, yet the disciplinary silos prevail and there is little communication and cooperation between the disaster management, environment, and development communities. One catastrophic event, such as an earthquake, tsunami, or cyclone, can destroy infrastructure, people’s lives and livelihoods, and set back development. Similarly, slow onset disasters—often associated with global climate change—pose threats to development, livelihoods, food security, and long-term sustainable development. This book is uniquely aimed at bridging the gaps between the environmental, development, and disaster management communities. It traces the evolution of concepts and practice and highlights the linkages between natural disasters and sustainable development in key sectors, including food security, health, and water. The book includes case studies from the field highlighting the complex issues that challenge sustainable development and disaster risk management in practice. It draws policy conclusions for the global community based on state-of-the art knowledge from research and practice. The primary target groups for the book are researchers, including graduate students, in the fields of environment and sustainable development, geography, disaster risk reduction, and climate change studies. The second target group comprises practitioners and policymakers working in national and international organizations, the private sector, and civil society.
| Author | : Zhenlong Li |
| Publisher | : Routledge |
| Total Pages | : 233 |
| Release | : 2020-12-17 |
| Genre | : Social Science |
| ISBN | : 1000261530 |
Social Sensing and Big Data Computing for Disaster Management captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically, analysed within this book are some of the promises and pitfalls of social sensing data for disaster relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems. Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all citizens to become part of a large sensor network which is low cost, more comprehensive, and always broadcasting situational awareness information. However, data collected with social sensing is often massive, heterogeneous, noisy, and unreliable in some aspects. It comes in continuous streams, and often lacks geospatial reference information. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. Meanwhile, big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion. This book was originally published as a special issue of the International Journal of Digital Earth.
