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.

This book comprises proceedings of the 28th International Conference on Hydraulics, Water Resources, River and Coastal Engineering (HYDRO 2023). It focuses on emerging opportunities and challenges in the field of soft computing and geospatial techniques in water resources engineering. The book covers a range of topics including, but not limited to, satellite-derived data for hydrologic applications, Geospatial Information System (GIS) and Remote Sensing (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. The book presents these topics in the form of illustrations and tables, thereby providing the readers with an in-depth insight into the recent research. It also addresses fundamental concepts and studies in the field of soft computing and geospatial techniques in water resources engineering, making it a valuable resource for researchers and professionals working in the fields of hydraulics, water resources and coastal engineering.

The Logic Scoring of Preference (LSP) method is a part of general multicriteria decision making approach that has origins in the soft computing. The method can model simultaneity, replaceability, and a wide range of other aggregators to suit various evaluation objectives. As soft computing method, LSP is based on fuzzy reasoning and can aggregate an unlimited amount of inputs without loss of significance. The main objective of this research is to develop and test integrated methods that use LSP, complex systems theory and geographic information systems (GIS) to model urban land-use change. In this research study LSP is integrated into a GIS to determine land-use suitability and is integrated into both cellular automata (CA) and agent-based models (ABMs) to simulate urban growth at both regional and local spatial scales. LSP approaches were implemented with geospatial datasets for Metro Vancouver, Canada and several scenarios of land-use change have been created.

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

Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data. Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information. With chapters contributed by experts in geoinformatics and in domains such as computing and engineering, the book provides an understanding of the challenges and issues of big data in geoinformatics applications. The book is a single collection of current and emerging techniques, technologies, and tools that are needed to collect, analyze, manage, process, and visualize geospatial big data.

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.

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.

This book explores cutting-edge methods combining geospatial technologies and artificial intelligence related to several fields such as smart farming, urban planning, geology, transportation, and 3D city models. It introduces techniques which range from machine and deep learning to remote sensing for geospatial data analysis. The book consists of two main parts that include 13 chapters contributed by promising authors. The first part deals with the use of artificial intelligence techniques to improve spatial data analysis, whereas the second part focuses on the use of artificial intelligence with remote sensing in various fields. Throughout the chapters, the interest for the use of artificial intelligence is demonstrated for different geospatial technologies such as aerial imagery, drones, Lidar, satellite remote sensing, and more. The work in this book is dedicated to the scientific community interested in the coupling of geospatial technologies and artificial intelligence and exploring the synergetic effects of both fields. It offers practitioners and researchers from academia, the industry and government information, experiences and research results about all aspects of specialized and interdisciplinary fields on geospatial intelligence.

The Logic Scoring of Preference (LSP) method is based on soft computing principles for complex spatial decision-making that integrates large number of criteria and capture human logic reasoning. The main objective of this study is to develop, implement and apply the LSP method in the Geographic Information System (GIS) environment for the land suitability evaluation for high-density urban development. Two different stakeholders, urban developer and urban planner, were considered. The geospatial datasets of Metro Vancouver Region, Canada, were used to implement the GIS-based LSP method. Several LSP aggregator groups have been compared and the results indicate that there are differences between the two stakeholder's perspectives on suitable locations for high-rise urban development. The GIS-LSP method provides an effective way for identifying the best location for high density urban developments and thus contribute to more sustainable urban practices that can minimize the impact of the urban sprawl.

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.