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This book presents an overview of modeling and simulation of environmental systems via diverse research problems and pertinent case studies. It is divided into four parts covering sustainable water resources modeling, air pollution modeling, Internet of Things (IoT) based applications in environmental systems, and future algorithms and conceptual frameworks in environmental systems. Each of the chapters demonstrate how the models, indicators, and ecological processes could be applied directly in the environmental sub-disciplines. It includes range of concepts and case studies focusing on a holistic management approach at the global level for environmental practitioners. Features: Covers computational approaches as applied to problems of air and water pollution domain. Delivers generic methods of modeling with spatio-temporal analyses using soft computation and programming paradigms. Includes theoretical aspects of environmental processes with their complexity and programmable mathematical approaches. Adopts a realistic approach involving formulas, algorithms, and techniques to establish mathematical models/computations. Provides a pathway for real-time implementation of complex modeling problem formulations including case studies. This book is aimed at researchers, professionals and graduate students in Environmental Engineering, Computational Engineering/Computer Science, Modeling/Simulation, Environmental Management, Environmental Modeling and Operations Research.
The present book describes the methodology to set up agent-based models and to study emerging patterns in complex adaptive systems resulting from multi-agent interaction. It offers the application of agent-based models in demography, social and economic sciences and environmental sciences. Examples include population dynamics, evolution of social norms, communication structures, patterns in eco-systems and socio-biology, natural resource management, spread of diseases and development processes. It presents and combines different approaches how to implement agent-based computational models and tools in an integrative manner that can be extended to other cases.
This volume contains 20 contributions to the 1st GAMM-Seminar at ICA Stuttgart, which was held in Stuttgart, October 12 - 13, 1995. In the field of environmental sciences, numerical procedures for the simulation of ecological problems are growing increasingly topical. The solution of typical problems in environmental research is closely connected with numerical supercomputing. The main subject of the seminar was the modeling and numerical simulation of ground water and soil water. Further topics were multi-scale modeling, special discretization schemes, adaptivity, multi-grid methods, heterogenity, parameter identification, homogenization, density driven groundwater flow, and coupling of transport and chemistry.
Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. - Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography - Provides an overview, methods and case studies for each application - Expresses concepts and methods at an appropriate level for both students and new users to learn by example
This book introduces numerical methods for processing datasets which may be of any form, illustrating adequately computational resolution of environmental alongside the use of open source libraries. This book solves the challenges of misrepresentation of datasets that are relevant directly or indirectly to the research. It illustrates new ways of screening datasets or images for maximum utilization. The adoption of various numerical methods in dataset treatment would certainly create a new scientific approach. The book enlightens researchers on how to analyse measurements to ensure 100% utilization. It introduces new ways of data treatment that are based on a sound mathematical and computational approach.
Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code.
This book provides a broad overview of essential features of subsurface environmental modelling at the science-policy interface, offering insights into the potential challenges in the field of subsurface flow and transport, as well as the corresponding computational modelling and its impact on the area of policy- and decision-making. The book is divided into two parts: Part I presents models, methods and software at the science-policy interface. Building on this, Part II illustrates the specifications using detailed case studies of subsurface environmental modelling. It also includes a systematic research overview and discusses the anthropogenic use of the subsurface, with a particular focus on energy-related technologies, such as carbon sequestration, geothermal technologies, fluid and energy storage, nuclear waste disposal, and unconventional oil and gas recovery.
The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data. Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Lab manual in R is available separately
Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management addresses the need for a comprehensive book that focuses on multi-hazard assessments, natural and manmade hazards, and risk management using new methods and technologies that employ GIS, artificial intelligence, spatial modeling, machine learning tools and meta-heuristic techniques. The book is clearly organized into four parts that cover natural hazards, environmental hazards, advanced tools and technologies in risk management, and future challenges in computer applications to hazards and risk management. Researchers and professionals in Earth and Environmental Science who require the latest technologies and advances in hazards, remote sensing, geosciences, spatial modeling and machine learning will find this book to be an invaluable source of information on the latest tools and technologies available. - Covers advanced tools and technologies in risk management of hazards in both the Earth and Environmental Sciences - Details the benefits and applications of various technologies to assist researchers in choosing the most appropriate techniques for purpose - Expansively covers specific future challenges in the use of computers in Earth and Environmental Science - Includes case studies that detail the applications of the discussed technologies down to individual hazards
Graphs, networks and agent-based modeling are the most thriving and attracting sciences used in ecology and environmental sciences. As such, this book is the first comprehensive treatment of the subject in the areas of ecology and environmental sciences. From this integrated and self-contained book, researchers, university teachers and students will be provided with an in-depth and complete insight on knowledge, methodology and recent advances of graphs, networks and agent-based-modeling in ecology and environmental sciences. Java codes and a standalone software package will be presented in the book for easy use for those not familiar with mathematical details.