Manish Kumar Goyal

This book offers a comprehensive overview of water quality assessment and wetland health monitoring in India's Ramsar sites using remote sensing and artificial intelligence. Featuring detailed methodologies and case studies from both coastal and inland wetlands, it highlights innovative technologies and strategies for monitoring, restoration, and sustainable management. With a focus on practical applications, the book emphasizes the role of policy, technology, and community engagement in wetland conservation, serving as a valuable resource for researchers, practitioners, and decision-makers involved in ecosystem protection.

This book explores Key Biodiversity Areas (KBAs) in India, addressing the challenges and proposing solutions for the conservation of these critical ecosystems. It examines the pivotal role of KBAs in biodiversity conservation and their significance in the global context, offering data-driven analyses of deforestation, habitat fragmentation, and forest loss. Utilizing open-source data, the book assesses the extent of habitat destruction, investigates the frequency and effects of fires on biodiversity and ecosystem services, and proposes actionable fire management strategies. The authors also explores the impact of artificial lighting on wildlife within KBAs, illustrating how human development alters natural habitats and offering mitigation measures. By integrating technology and policy, the book emphasizes the application of remote sensing and data analytics to develop evidence-based conservation strategies. It also charts future directions for monitoring KBAs, highlighting the potential of emerging technologies in conservation efforts.

In the face of unprecedented challenges in managing water resources, the integration of artificial intelligence (AI) emerges as a revolutionary force, reshaping the landscape of water conservation, treatment, irrigation, policy formulation, watershed management, and the monitoring of groundwater and surface water. This book explores the transformative role of AI in the water domain, exploring cutting-edge applications and innovative solutions that promise to address pressing issues in sustainable water management. As we navigate the complexities of a changing climate, population growth, and urbanization, the chapters within this book offer insights into how AI technologies can enhance efficiency, optimize resource utilization, and provide data-driven strategies for ensuring the resilience and sustainability of our vital water ecosystems. From intelligent water treatment systems to precision agriculture and policy decision support, each chapter unfolds a narrative of AI-driven advancements, providing a comprehensive guide for researchers, practitioners, and policymakers navigating the intersection of artificial intelligence and water management.

This book thoroughly examines aerosol pollution and aerosol atmospheric rivers (narrow corridors of concentrated suspended aerosols in the sky), exploring their significant effects on human health, the environment, and global climate. Readers will find detailed discussions on these phenomena' sources, composition, patterns, and advanced methods for their detection, monitoring, and mitigation. Each chapter examines the complex dynamics of aerosol atmospheric rivers and the use of data mining and artificial intelligence in analyzing aerosol pollution. The book also highlights the interactions between aerosol pollution, aerosol atmospheric rivers, and particulate matter concentrations with associated risk, offering practical adaptation, mitigation, and resilience strategies.

This book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). It begins with the historical background and mechanisms governing AR formation, giving insights into the global and regional perspectives of ARs, observing their varying manifestations across different geographical contexts. The book explores the key characteristics of ARs, from their frequency and duration to intensity, unraveling the intricate relationship between atmospheric rivers and precipitation. The book also focus on the intersection of ARs with large-scale climate oscillations, such as El Niño and La Niña events, the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO). The chapters help understand how these climate phenomena influence AR behavior, offering a nuanced perspective on climate modeling and prediction. The book also covers artificial intelligence (AI) applications, from pattern recognition to prediction modeling and early warning systems. A case study on AR prediction using deep learning models exemplifies the practical applications of AI in this domain. The book culminates by underscoring the interdisciplinary nature of AR research and the synergy between atmospheric science, climatology, and artificial intelligence