Advanced Numerical Modeling And Data Assimilation Techniques For Tropical Cyclone Predictions
Download Advanced Numerical Modeling And Data Assimilation Techniques For Tropical Cyclone Predictions PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Advanced Numerical Modeling And Data Assimilation Techniques For Tropical Cyclone Predictions book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Predictions
This book deals primarily with monitoring, prediction and understanding of Tropical Cyclones (TCs). It was envisioned to serve as a teaching and reference resource at universities and academic institutions for researchers and post-graduate students. It has been designed to provide a broad outlook on recent advances in observations, assimilation and modeling of TCs with detailed and advanced information on genesis, intensification, movement and storm surge prediction. Specifically, it focuses on (i) state-of-the-art observations for advancing TC research, (ii) advances in numerical weather prediction for TCs, (iii) advanced assimilation and vortex initialization techniques, (iv) ocean coupling, (v) current capabilities to predict TCs, and (vi) advanced research in physical and dynamical processes in TCs. The chapters in the book are authored by leading international experts from academic, research and operational environments. The book is also expected to stimulate critical thinking for cyclone forecasters and researchers, managers, policy makers, and graduate and post-graduate students to carry out future research in the field of TCs.
Advanced GIScience in Hydro-Geological Hazards
In recent decades, natural hazards have increasingly threatened lives, livelihoods, and economies, with annual losses totalling billions of dollars globally. According to the Insurance Information Institute (III) and the Zebra, USA, natural disaster losses reached $74.4 billion in 2020, and an average of 6,800 natural disasters occur each year, claiming around 1.35 million lives. Hydrological and geological hazards, in particular, have significant societal and environmental impacts, making them critical areas of research. Understanding and mitigating these hazards is vital for developing legal mechanisms related to environmental restoration, societal improvements, and sustainable development. Modern technologies and earth observation data play a crucial role in disaster monitoring, prediction, modelling, and management. Recent advancements in geoinformation science have introduced multi-source data for natural hazards research. In addition, cutting-edge methods such as machine learning, deep learning, and big data science offer powerful tools for in-depth studies of natural hazards through remote sensing and geoinformatics. This book, Advanced GIScience in Hydro-Geological Hazards, presents up-to-date contributions on applying advanced GIScience to research various hydro-geological hazards, including floods, landslides, tropical cyclones, soil erosion, coastal erosion, riverbank erosion, coastal area vulnerability, drought, wetlands shrinking etc. It also explores multi-hazard studies using SAR, GNSS, and other innovative methods. The chapters focus on integrating artificial intelligence, machine learning techniques, and remote sensing to enhance preparedness, response, and resilience against these hazards. Targeting a broad audience of academics, scientists, students, environmentalists, government agencies, disaster planners, and GIS experts, this book aims to showcase the latest advancements in GIScience for assessing and managing hydro-geological hazards. It offers strategies for disaster risk reduction and capacity building, providing readers with the knowledge needed to address pressing environmental challenges.
Neural Information Processing
The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 11301, is organized in topical sections on deep neural networks, convolutional neural networks, recurrent neural networks, and spiking neural networks.