Saltar al contenido principal
Elsevier

Libros en Computadoras en Geociencias

  • Landslide Science

    Geospatial Advances in Monitoring, Modeling, and Management
    • 1 Edición
    • Christos Polykretis + 3 más
    • Inglés
    Landslide Science: Geospatial Advances in Monitoring, Modeling, and Management presents a detailed exploration of the latest advancements in landslide research, offering a thorough examination of the latest geospatial technologies and methodologies. The book begins with a comprehensive exploration of landslides, starting with an understanding of their nature, including characteristics, types, and historical perspectives. Factors triggering or influencing landslides are examined, such as rainfall, earthquakes, and topographical influences. Remote sensing techniques for landslide investigation are detailed, covering detection, monitoring, and imagery data processing methods using various sensors and technologies. Spatial landslide assessment and mapping are discussed, including inventory mapping, susceptibility assessment, and hazard assessment utilizing different approaches and uncertainty validation methods. Engineering-based landslide analysis focuses on geotechnical aspects, hydrogeological influences, and methodologies for simulations and slope stability analysis. The book also addresses landslide impacts and risk management strategies, covering vulnerability assessment, risk analysis, environmental consequences, prevention, mitigation strategies, and community engagement. Advancing science and future directions in landslide research are explored, including trends in landslide occurrence, early warning systems, technological innovations, and remaining challenging issues in landslide science. This comprehensive guide offers valuable insights and strategies for understanding, assessing, and managing landslides for a more resilient future.

  • Multimodal Remote Sensing Fusion and Classification

    Algorithms and Applications
    • 1 Edición
    • Man-On Pun + 1 más
    • Inglés
    Multimodal Remote Sensing Data Fusion for Classification: Algorithms and Applications offers a comprehensive overview of Earth observation data fusion, focusing on multimodal remote sensing. It presents state-of-the-art algorithms and practical applications that enhance understanding of Earth's dynamic processes. Through detailed analysis, case studies, and practical examples, this book equips readers with the necessary tools to effectively utilize multimodal data fusion for land cover and land use classification, as well as environmental monitoring, making it an invaluable resource for those in remote sensing and Earth sciences.Furthermore... the book is tailored for Masters and Doctorate students, scientists, and professionals in remote sensing, geography, and Earth sciences. It delves into the integration and analysis of multimodal remote sensing data, offering insights into sustainable solutions for environmental challenges. This comprehensive coverage ensures readers are well-versed in the cutting-edge techniques and methodologies required for advanced Earth observation and classification tasks.

  • Reshaping Geotechnical Engineering with Machine Learning

    Theory, Applications, and Innovations
    • 1 Edición
    • Divesh Ranjan Kumar + 3 más
    • Inglés
    Reshaping Geotechnical Engineering with Machine Learning: Theory, Applications, and Innovations explores the transformative impact of machine learning (ML) on the field of geotechnical engineering. The book begins by examining the broad applications of ML in key areas such as foundation engineering and slope stability, demonstrating how advanced algorithms can enhance predictive accuracy and decision-making. It emphasizes the importance of robust data acquisition and preprocessing techniques, which are critical for the successful implementation of ML models in geotechnical contexts. The text examines the use of machine learning for predicting soil behavior, a complex challenge in geotechnical engineering, and highlights its role in risk assessment and management.In addition, it also addresses the integration of ML with finite element modeling to improve the analysis of tunnel and underground stability. The applications of machine learning in understanding geotechnical materials further showcase the versatility of these techniques. It also covers experimental investigations, including laboratory and field studies, which provide essential data for model training and validation. Through detailed case studies, the book illustrates practical implementations of machine learning, bridging theory and real-world problem-solving.

  • Big Data Mining and Machine Learning in Geoscience

    • 1 Edición
    • Yongzhang Zhou + 4 más
    • Inglés
    Big Data Mining and Machine Learning in Geoscience offers a comprehensive overview of techniques and applications of data science in the geosciences. Sections cover essential, foundational concepts in data cleaning and preprocessing, thus ensuring the quality and reliability of geoscientific data. Next, the book explores dimensionality reduction methods designed to simplify high-dimensional data without losing critical information. The text covers classification and prediction techniques that enable the identification of patterns and forecasting of geological phenomena, and graphical data processing and handling of infinite stream data and time series are highlighted, along with their importance in real-time monitoring and dynamic systems analysis.In addition, the book explores advanced machine learning and deep learning methods, showcasing their transformative impact on geoscientific research. It also introduces knowledge graphs and large language models as emerging tools that enhance data integration, interpretation, and discovery. AI-driven geology is presented as a forward-looking approach that leverages artificial intelligence to revolutionize traditional geological practices, offering improved accuracy and insight. Throughout, practical examples and case studies illustrate how these technologies can be applied to solve complex problems in geoscience.

  • EnviroGIS

    Geographic Information Systems in Environmental Analysis
    • 1 Edición
    • Jesús Rodrigo-Comino
    • Inglés
    EnviroGIS: Geographic Information Systems in Environmental Analysis is a comprehensive and timely textbook that provides a foundation for those considering further research in GIS, environmental sciences, or geosciences. It offers a thorough understanding of how to initiate a GIS course in Geosciences, introducing the importance of GIS by taking a step-by-step approach for delineating the study area. This book provides diverse ideas, data, and practical examples for both replication and reference to students, scientists, and lecturers engaging with GIS and environmental science, particularly areas focused on Earth Surface Processes using tools like ArcGIS Pro, QGIS, Terrset/Idrissi, Google Earth Engine, R, and Pix4D.In the first part, the textbook delineates the importance of GIS. Moving on, in the second part it focuses on the most relevant topics in environmental analysis. It outlines the preparation of seminars related to changing scales, soil, plant, and catchment dynamic processes, such as erosion and interpolations. Covering topics such as data mining, and connectivity processes, EnviroGIS highlights the way in which Artificial Intelligence enables the creation of accurate forecasts of challenging-to-predi... Earth surface processes. Additionally, the biosphere is delved into to show how GIS courses assess fauna and vegetation within natural or human ecosystems. The final part of the book is centered on specific cases, considering different GIS data formats including shapefiles, multilayer projects, and recent natural hazards or disasters, such as volcanic eruptions or hillslope degradation due to human activities.

  • Handbook of Satellite Land Products

    • 1 Edición
    • Shunlin Liang + 1 más
    • Inglés
    Handbook of Satellite Land Products compiles the key information of as many satellite products as possible for each essential land variable. It provides a comprehensive list of openly accessible high-level satellite products that characterize the essential land surface variables of the Earth system, including the original data sources, inversion algorithms, product characteristics, validation accuracy, and any existing applications. Each product includes satellite data inversion methodology and high-level product generation and application information, allowing for comparison of different products and selection of the correct ones for different applications.This book will help researchers, professors, and graduate students grasp the knowledge of existing satellite land products, understand the advantages and limitations, and follow the practical application examples.

  • Remote Sensing, Big Data, and GeoAI

    Exploring Applications with Geospatial Insights
    • 1 Edición
    • Erin Bunting + 3 más
    • Inglés
    Remote Sensing, Big Data, and GeoAI: Exploring Applications with Geospatial Insights is an in-depth analysis of the transformative power of AI and Big Data in remote sensing. This book provides readers with the knowledge and tools to utilize these technologies to enhance decision-making and analysis. Starting from fundamental concepts, it progresses to advanced applications, offering accessible explanations and real-world examples to bridge the gap between theory and practice. The book uses a structured format to balance theoretical knowledge with immersive case studies, giving readers a deeper understanding of practical implications.It also covers ethical and legal considerations, making it an invaluable resource for researchers, professionals, and students keen on using AI and Big Data techniques in remote sensing to solve complex geospatial challenges.

  • The Geoinformatics Frontier

    AI, Big Data, and Crowdsourced Technologies
    • 1 Edición
    • Kleomenis Kalogeropoulos + 3 más
    • Inglés
    The Geoinformatics Frontier: AI, Big Data, and Crowdsourced Technologies tackles the critical challenge of integrating Geoinformatics, AI, Big Data, and VGI; offering a comprehensive introduction to these pivotal concepts, the book elucidates their foundations and relevance to Geoinformatics. It approaches builds on the theory discussed with practical guidance, examples, and detailed case studies; equipping readers with the knowledge needed to effectively implement them. The book presents case studies spanning various sectors, showcasing how the technologies can be successfully employed to address intricate spatial issues and facilitate well-informed decision-making for the complexities of managing large-scale spatial datasets. It also provides indispensable insights into data collection, storage, quality control, and fusion techniques, offering practical solutions to the challenges of data storage, processing, and analysis. The Geoinformatics Frontier serves as an indispensable guide, bridging the gap in understanding and practice for geospatial scientists, empowering readers to harness the transformative potential of Geoinformatics and advanced computer technologies.

  • Introduction and Applications of Machine Learning in Geotechnics

    • 1 Edición
    • Zong Woo Geem + 5 más
    • Inglés
    Introduction and Applications of Machine Learning in Geotechnics offers a comprehensive exploration of machine learning methodologies and their diverse applications in geotechnical engineering. The book begins with a detailed review of machine learning methods tailored for geotechnical applications, setting the foundation for subsequent chapters. Regression models are utilized to predict shear wave velocities while optimization-based approaches are employed to determine the optimal dimensions of reinforced concrete (RC) retaining walls. The book further explores the identification of gravelly soil through optimized machine learning models and predicts stress-strain responses using data from simple shear tests.Additionally, it outlines the forecasting of liquefaction events triggered by seismic activities and estimates the uniaxial compressive strength of soil using machine learning techniques. The prediction of vertical effective stress and specific penetration resistance is examined to enhance soil characterization and geotechnical analyses. The authors' provide valuable insights for geotechnical engineers and researchers seeking to leverage advanced computational tools for enhanced geotechnical assessments and design processes.

  • Advances in Computational Geomechanics

    Advanced Computational Techniques and Methodologies in Geotechnical Engineering
    • 1 Edición
    • Mohamed Shahin + 1 más
    • Inglés
    Advances in Computational Geomechanics: Advanced Computational Techniques and Methodologies in Geotechnical Engineering provides a comprehensive overview of cutting-edge computational methodologies in geotechnical engineering. The first part examines stochastic, probabilistic, and reliability analyses in geotechnical engineering, covering stochastic methods, probabilistic approaches to soil characterization, reliability analysis in geotechnical design, and risk assessment and management in geotechnical projects. The second part delves into artificial intelligence (AI) and machine learning applications in geotechnical engineering, including machine learning algorithms for geotechnical data analysis, AI-based predictive models for soil behavior and properties, AI in geotechnical risk and decision-making, and data-driven approaches for soil classification and site characterization. The third part focuses on numerical modeling and analysis techniques, such as the Finite Element Method (FEM), Finite Difference Method (FDM), Discrete Element Method (DEM), and explores hybrid numerical methods and future directions in computational geomechanics. This book serves as a valuable resource for geotechnical engineers, researchers, and practitioners seeking to leverage advanced computational tools for geomechanical analyses and design.