Computational Intelligence Techniques In Earth And Environmental Sciences

Computational intelligence techniques have enjoyed growing interest in recent decades among the earth and environmental science research communities for their powerful ability to solve and understand various complex problems and develop novel approaches toward a sustainable earth. This book compiles a collection of recent developments and rigorous applications of computational intelligence in these disciplines. Techniques covered include artificial neural networks, support vector machines, fuzzy logic, decision-making algorithms, supervised and unsupervised classification algorithms, probabilistic computing, hybrid methods and morphic computing. Further topics given treatment in this volume include remote sensing, meteorology, atmospheric and oceanic modeling, climate change, environmental engineering and management, catastrophic natural hazards, air and environmental pollution and water quality. By linking computational intelligence techniques with earth and environmental science oriented problems, this book promotes synergistic activities among scientists and technicians working in areas such as data mining and machine learning. We believe that a diverse group of academics, scientists, environmentalists, meteorologists and computing experts with a common interest in computational intelligence techniques within the earth and environmental sciences will find this book to be of great value.

How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

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

How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

This book provides a comprehensive yet fresh perspective for the cutting-edge CI-oriented approaches in water resources planning and management. The book takes a deep dive into topics like meta-heuristic evolutionary optimization algorithms (e.g., GA, PSA, etc.), data mining techniques (e.g., SVM, ANN, etc.), probabilistic and Bayesian-oriented frameworks, fuzzy logic, AI, deep learning, and expert systems. These approaches provide a practical approach to understand and resolve complicated and intertwined real-world problems that often imposed serious challenges to traditional deterministic precise frameworks. The topic caters to postgraduate students and senior researchers who are interested in computational intelligence approach to issues stemming from water and environmental sciences.

This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN – the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (...) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (...) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (...) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promises to advance a deeper understanding of best modeling practices in environmental science.” (Dr. Sue Ellen Haupt, National Center for Atmospheric Research, Boulder, USA) “Vladimir Krasnopolsky has written an important and wonderful book on applications of neural networks to replace complex and expensive computational algorithms within Earth System Science models. He is uniquely qualified to write this book, since he has been a true pioneer with regard to many of these applications. (...) Many other examples of creative emulations will inspire not just readers interested in the Earth Sciences, but any other modeling practitioner (...) to address both theoretical and practical complex problems that may (or will!) arise in a complex system." ” (Prof. Eugenia Kalnay, University of Maryland, USA)

A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

A radical and challenging book which argues that artificial intelligence needs a completely different set of foundations, based on ecological intelligence rather than human intelligence, if it is to deliver on the promise of a better world. This can usher in the greatest transformation in human history, an age of re-integration. Our very existence is dependent upon our context within the Earth System, and so, surely, artificial intelligence must also be grounded within this context, embracing emergence, interconnectedness and real-time feedback. We discover many positive outcomes across the societal, economic and environmental arenas and discuss how this transformation can be delivered. Key Features: Identifies a key weakness in current AI thinking, that threatens any hope of a better world. Highlights the importance of realizing that systems theory is an essential foundation for any technology that hopes to positively transform our world. Emphasizes the need for a radical new approach to AI, based on ecological systems. Explains why ecosystem intelligence, not human intelligence, offers the best framework for AI. Examines how this new approach will impact on the three arenas of society, environment and economics, ushering in a new age of re-integration.