Kanwarpreet Singh – författare
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8 produkter
8 produkter
Inbunden, Engelska, 2026
1 728 kr
Skickas inom 10-15 vardagar
Natural disasters such as earthquakes, floods, landslides, and extreme weather events pose serious threats to industrial operations, leading to disruptions, infrastructure damage, and financial losses. With increasing climate variability, industries must adopt proactive risk assessment and mitigation strategies to enhance resilience and ensure business continuity.This book offers a comprehensive framework for assessing and mitigating natural hazard risks in industrial environments through Industrial Internet of Things (IIoT) technologies. It examines real-time monitoring, sensor networks, remote sensing, and automated control systems to strengthen early warning mechanisms and disaster response. Presented as a step-by-step guide, the book addresses industrial vulnerability, hazard-specific risks, IIoT integration for smart monitoring, and resilience planning for infrastructure and supply chains. It combines actionable strategies with case studies and best practices to help safeguard industries against natural hazards.Designed for industrial safety experts, disaster risk managers, environmental planners, policymakers, and researchers, this resource equips readers with the tools to build disaster-proof operations - whether shaping policy, designing resilient infrastructure, or managing industrial safety.
E-bok
Engelska, 20262 207 kr
Läs direkt efter köp
Natural disasters such as earthquakes, floods, landslides, and extreme weather events pose serious threats to industrial operations, leading to disruptions, infrastructure damage, and financial losses. With increasing climate variability, industries must adopt proactive risk assessment and mitigation strategies to enhance resilience and ensure business continuity.This book offers a comprehensive framework for assessing and mitigating natural hazard risks in industrial environments through Industrial Internet of Things (IIoT) technologies. It examines real-time monitoring, sensor networks, remote sensing, and automated control systems to strengthen early warning mechanisms and disaster response. Presented as a step-by-step guide, the book addresses industrial vulnerability, hazard-specific risks, IIoT integration for smart monitoring, and resilience planning for infrastructure and supply chains. It combines actionable strategies with case studies and best practices to help safeguard industries against natural hazards.Designed for industrial safety experts, disaster risk managers, environmental planners, policymakers, and researchers, this resource equips readers with the tools to build disaster-proof operations - whether shaping policy, designing resilient infrastructure, or managing industrial safety.
Häftad, Tyska, 2025
447 kr
Skickas inom 5-8 vardagar
Häftad, Polska, 2025
447 kr
Skickas inom 5-8 vardagar
Häftad, Franska, 2025
447 kr
Skickas inom 5-8 vardagar
Häftad, Italienska, 2025
432 kr
Skickas inom 5-8 vardagar
Häftad, Portugisiska, 2025
447 kr
Skickas inom 5-8 vardagar
E-bok
Engelska, 20261 042 kr
Läs direkt efter köp
Machine Learning and Spatial Optimisation is an exploration positioned at the intersection of environmental science, geospatial technology, and data analytics, exploring how advanced computational methods and spatial data analysis can address critical environmental challenges.The chapters progress from foundational concepts to practical case studies in spatial data and GIS workflows to real-world applications, including air quality monitoring, water resource management, land-use analysis, biodiversity conservation, and disaster risk assessment.With a strong focus on real-world implementation, the book bridges theory and practice by offering methodological insights, policy relevance, and data-driven strategies for sustainable environmental management.Key Features:-Integration of machine learning with GIS and spatial analysis-Coverage of major environmental challenges and applications-Real-world case studies for monitoring, prediction, and planning-Focus on decision support, policy insights, and sustainability-Practical approaches to data-driven environmental management