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It is widely assumed that humanity should be able to learn from calamities (e.g., emergencies, disasters, catastrophes) and that the affected individuals, groups, and enterprises, as well as the concerned (disaster-) management organizations and institutions for prevention and mitigation, will be able to be better prepared or more efficient next time. Furthermore, it is often assumed that the results of these learning processes are preserved as "knowledge" in the collective memory of a society, and that patterns of practices were adopted on this base. Within history, there is more evidence for the opposite: Analyzing past calamities reveals that there is hardly any learning and, if so, that it rarely lasts more than one or two generations. This book explores whether learning in the context of calamities happens at all, and if learning takes place, under which conditions it can be achieved and what would be required to ensure that learned cognitive and practical knowledge will endure on a societal level. The contributions of this book include various fields of scientific research: history, sociology, geography, psychoanalysis, psychiatry, development studies and political studies, as well as disaster research and disaster risk reduction research.
Initial priorities for U.S. participation in the International Decade for Natural Disaster Reduction, declared by the United Nations, are contained in this volume. It focuses on seven issues: hazard and risk assessment; awareness and education; mitigation; preparedness for emergency response; recovery and reconstruction; prediction and warning; learning from disasters; and U.S. participation internationally. The committee presents its philosophy of calls for broad public and private participation to reduce the toll of disasters.
In a fast-changing, globalising world, the teaching and implementation of a curriculum for Education for Sustainability (EfS) has been a challenge for many teachers. Issues in Teaching and Learning of Education for Sustainability highlights the issues and challenges educators and academics face in implementing EfS and gives examples of what an EfS curriculum may look like and how some institutions translate the theory into practice. Organised into three parts, the volume looks at: the who (EfS for whom), the what (EfS curriculum) and the how (translating from theory to practice). The concluding chapter provides ideas and directions on where the world can proceed regarding sustainability education and how it can help in the teaching and learning of sustainability. Considering social issues such as poverty, education, health, culture and the use of natural resources, this book proposes a different path towards Education for Sustainability. Providing concrete data on the realisation of sustainable development, Issues in Teaching and Learning of Education for Sustainability will be of interest to geographers, geography educators and professionals concerned with Education for Sustainability.
WINNER of the 2017 Firecracker Award for Nonfiction from CLMP A collection of linked essays concerned with the life and mind of the writer by one of the most original voices in contemporary literature. Each essay takes a day as its point of inquiry, observing the body as it moves through time, architecture, and space, gradually demanding a new logic and level of consciousness from the narrator and reader.
The industrialized world has long been rocked by economic crises, often caused by policy makers who are guided by ideology rather than cold, hard analysis. WRONG examines the worst economic policy blunders of the last 250 years, providing a valuable guide book for policy makers... and the citizens who elect them.
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.
Historical disaster research is still a young field. This book discusses the experiences of natural disasters in different cultures, from Europe across the Near East to Asia. It focuses on the pre-industrial era and on the question of similarities, differences and transcultural dynamics in the cultural handling of natural disasters. Which long-lasting cultural patterns of perception, interpretation and handling of disasters can be determined? Have specific types of disasters changed the affected societies? What have people learned from disasters and what not? What adaptation and coping strategies existed? Which natural, societal and economic parameters play a part? The book not only reveals the historical depth of present practices, but also reveals possible comparisons that show globalization processes, entanglements and exchanges of ideas and practices in pre-modern times.
"The Ostrich Paradox boldly addresses a key question of our time: Why are we humans so poor at dealing with disastrous risks, and what can we humans do about it? It is a must-read for everyone who cares about risk." —Daniel Kahneman, winner of the Nobel Prize in Economics and author of Thinking, Fast and Slow We fail to evacuate when advised. We rebuild in flood zones. We don't wear helmets. We fail to purchase insurance. We would rather avoid the risk of "crying wolf" than sound an alarm. Our ability to foresee and protect against natural catastrophes has never been greater; yet, we consistently fail to heed the warnings and protect ourselves and our communities, with devastating consequences. What explains this contradiction? In The Ostrich Paradox, Wharton professors Robert Meyer and Howard Kunreuther draw on years of teaching and research to explain why disaster preparedness efforts consistently fall short. Filled with heartbreaking stories of loss and resilience, the book addresses: •How people make decisions when confronted with high-consequence, low-probability events—and how these decisions can go awry •The 6 biases that lead individuals, communities, and institutions to make grave errors that cost lives •The Behavioral Risk Audit, a systematic approach for improving preparedness by recognizing these biases and designing strategies that anticipate them •Why, if we are to be better prepared for disasters, we need to learn to be more like ostriches, not less Fast-reading and critically important, The Ostrich Paradox is a must-read for anyone who wants to understand why we consistently underprepare for disasters, as well as private and public leaders, planners, and policy-makers who want to build more prepared communities.
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.