Geospatial And Soft Computing Techniques

This book comprises the proceedings of the 26th International Conference on Hydraulics, Water Resources and Coastal Engineering (HYDRO 2021) focusing on broad spectrum of emerging opportunities and challenges in the field of soft computing and geospatial techniques in water resources engineering. It covers a range of topics, including, but not limited to, satellite derived data for hydrologic applications, GIS and RS applications in water resources management, rainfall and streamflow prediction, hydro-informatics, data driven and artificial intelligent based hydrological modelling, optimization of water resources systems, etc. Presenting recent advances in the form of illustrations, tables, and text, it offers readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the field of Soft Computing and Geospatial Techniques in Water Resources Engineering, making it a valuable resource for both beginners and researchers wanting to further their understanding of hydraulics, water resources and coastal engineering.

Geoinformatics for Geosciences: Advanced Geospatial Analysis using RS, GIS and Soft Computing is a comprehensive guide to the methodologies and techniques that can be used in Earth observation data assessments, geospatial analysis, and soft computing in the geosciences. The book covers a variety of spatiotemporal problems and topics in the areas of the environment, geohazards, urban analysis, health, pollution, climate change, resources and geomorphology, among others. Sections cover environmental and climate issues, analysis of geomorphological data, hazard and disaster impacts, natural and human resources, the influence of environmental conditions, geohazards, climate change, geomorphological changes, etc., and socioeconomic challenges. Detailing up-to-date techniques in geoinformatics, this book offers in-depth, up-to-date methodologies for researchers and academics to understand how contemporary data can be combined with innovative techniques and tools in order to address challenges in the geosciences. Focuses on a variety of interdisciplinary applications using Earth observation data, GIS, and soft computing techniques to address various challenges in the geosciences Presents both innovative techniques and specific features of the practices described, thus allowing readers to choose the most appropriate method to apply to their specific problems Comprehensively details the state-of-the-art methodologies and decision-making tools included to ensure they are easily reproduceable

Over the past decade, since the publication of the first edition, there have been new advances in solving complex geoinformatics problems. Advancements in computing power, computing platforms, mathematical models, statistical models, geospatial algorithms, and the availability of data in various domains, among other things, have aided in the automation of complex real-world tasks and decision-making that inherently rely on geospatial data. Of the many fields benefiting from these latest advancements, machine learning, particularly deep learning, virtual reality, and game engine, have increasingly gained the interest of many researchers and practitioners. This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems. FEATURES Contains a comprehensive collection of advanced big data approaches, techniques, and technologies for geoinformatics problems Provides seven new chapters on deep learning models, algorithms, and structures, including a new chapter on how spatial metaverse is used to build immersive realistic virtual experiences Presents information on how deep learning is used for solving real-world geoinformatics problems This book is intended for researchers, academics, professionals, and students in such fields as computing and information, civil and environmental engineering, environmental sciences, geosciences, geology, geography, and urban studies.

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.

The digital revolution and the explosive growth of the internet have helped the collection of huge amounts of useful data of diverse characteristics, which is a valuable and intangible asset in any business of today. This book treats the new, emerging discipline of soft computing, which exploits this data through tolerance for imprecision and uncertainty to achieve solutions for complex problems. Soft computing methodologies include fuzzy sets, neural networks, genetic algorithms, Bayesian belief networks and rough sets, which are explored in detail through case studies and in-depth research. The advent of soft computing marks a significant paradigm shift in computing, with a wide range of applications and techniques which are presented and discussed in the chapters of this book.

This volume fills a research gap between the rapid development of High Performance Computing (HPC) approaches and their geospatial applications. With a focus on geospatial applications, the book discusses in detail how researchers apply HPC to tackle their geospatial problems. Based on this focus, the book identifies the opportunities and challenges revolving around geospatial applications of HPC. Readers are introduced to the fundamentals of HPC, and will learn how HPC methods are applied in various specific areas of geospatial study. The book begins by discussing theoretical aspects and methodological uses of HPC within a geospatial context, including parallel algorithms, geospatial data handling, spatial analysis and modeling, and cartography and geovisualization. Then, specific domain applications of HPC are addressed in the contexts of earth science, land use and land cover change, urban studies, transportation studies, and social science. The book will be of interest to scientists and engineers who are interested in applying cutting-edge HPC technologies in their respective fields, as well as students and faculty engaged in geography, environmental science, social science, and computer science.

Technological advancements have changed the way we think of traditional urban and spatial planning. The inclusion of conventional elements with modern technologies is allowing this field to advance at a rapid pace. Geospatial Technologies in Urban System Development: Emerging Research and Opportunities is a pivotal reference source for the latest research findings on the different tools and techniques ranging from mathematical sciences to spatial sciences which can be effective in unveiling the complexity of an urban system. Featuring extensive coverage on relevant areas such as urban traffic, remote sensing, and geographic information systems, this publication is an ideal resource for academics, researchers, graduate-level students, professionals, and policymakers in urban economy, regional planning, and information science disciplines.

Data science has recently gained much attention for a number of reasons, and among them is Big Data. Scientists (from almost all disciplines including physics, chemistry, biology, sociology, among others) and engineers (from all fields including civil, environmental, chemical, mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data. This book is designed to highlight the unique characteristics of geospatial data, demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data, and present select state-of-the-art geospatial data science techniques and how they are applied to various geoscience problems.