Intelligence Systems for Earth Environmental and Planetary Sciences

Intelligence Systems for Earth  Environmental and Planetary Sciences
Author: Hossein Bonakdari,Silvio José Gumiere
Publsiher: Elsevier
Total Pages: 0
Release: 2024-08-01
Genre: Science
ISBN: 0443132933

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Intelligence systems for Earth, Environmental and Planetary Sciences: Methods, Models and Applications provides cutting-edge theory and applications of modern-day artificial intelligence and data science in the Earth, environment and planetary science fields. The book is divided into three parts: Methods, covering the fundamentals of intelligence systems, along with an introduction to the preparation of datasets. Models, covering model development, data assimilation, and techniques in each field. Applications, presenting case studies of artificial intelligence and data science solutions to Earth, environmental and planetary sciences problems, as well as future perspectives. Intelligence systems for Earth, Environmental and Planetary Sciences will be of interest to students, academics and post-graduate professionals in the field of applied sciences, earth, environmental and planetary sciences, and would also serve as an excellent companion resource to courses studying artificial intelligence applications for theoretical and practical studies in Earth, environmental and planetary sciences.

Machine Learning in Earth Environmental and Planetary Sciences

Machine Learning in Earth  Environmental and Planetary Sciences
Author: Hossein Bonakdari,Isa Ebtehaj,Joseph D. Ladouceur
Publsiher: Elsevier
Total Pages: 390
Release: 2023-07-03
Genre: Science
ISBN: 9780443152856

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Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation. This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results. Describes how to develop different schemes of machine learning techniques and apply to Earth, environmental and planetary data Provides detailed, guided line-by-line examples using real-world data, including the appropriate MATLAB codes Includes numerous figures, illustrations and tables to help readers better understand the concepts covered

Computational Intelligence Techniques in Earth and Environmental Sciences

Computational Intelligence Techniques in Earth and Environmental Sciences
Author: Tanvir Islam,Prashant K. Srivastava,Manika Gupta,Xuan Zhu,Saumitra Mukherjee
Publsiher: Springer Science & Business Media
Total Pages: 266
Release: 2014-02-14
Genre: Science
ISBN: 9789401786423

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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.

Computers in Earth and Environmental Sciences

Computers in Earth and Environmental Sciences
Author: Hamid Reza Pourghasemi
Publsiher: Elsevier
Total Pages: 702
Release: 2021-09-22
Genre: Computers
ISBN: 9780323898614

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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

Dynamic Data Driven Environmental Systems Science

Dynamic Data Driven Environmental Systems Science
Author: Sai Ravela,Adrian Sandu
Publsiher: Springer
Total Pages: 360
Release: 2015-11-26
Genre: Computers
ISBN: 9783319251387

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This book constitutes the refereed proceedings of the First International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014, held in Cambridge, MA, USA, in November 2014.The 24 revised full papers and 7 short papers were carefully reviewed and selected from 62 submissions and cover topics on sensing, imaging and retrieval for the oceans, atmosphere, space, land, earth and planets that is informed by the environmental context; algorithms for modeling and simulation, downscaling, model reduction, data assimilation, uncertainty quantification and statistical learning; methodologies for planning and control, sampling and adaptive observation, and efficient coupling of these algorithms into information-gathering and observing system designs; and applications of methodology to environmental estimation, analysis and prediction including climate, natural hazards, oceans, cryosphere, atmosphere, land, space, earth and planets.

Recent Advances in Natural Methane Seep and Gas Hydrate Systems

Recent Advances in Natural Methane Seep and Gas Hydrate Systems
Author: Tamara Baumberger,Martin Scherwath,Ira Leifer,Samantha Joye
Publsiher: Frontiers Media SA
Total Pages: 214
Release: 2023-02-13
Genre: Science
ISBN: 9782889744473

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Artificial Intelligence and Data Science in Environmental Sensing

Artificial Intelligence and Data Science in Environmental Sensing
Author: Mohsen Asadnia,Amir Razmjou,Amin Beheshti
Publsiher: Academic Press
Total Pages: 326
Release: 2022-02-09
Genre: Computers
ISBN: 9780323905077

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Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications. Presents tools, connections and proactive solutions to take sustainability programs to the next level Offers a practical guide for making students proficient in modern electronic data analysis and graphics Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery

Artificial Intelligence Methods in the Environmental Sciences

Artificial Intelligence Methods in the Environmental Sciences
Author: Sue Ellen Haupt,Antonello Pasini,Caren Marzban
Publsiher: Springer Science & Business Media
Total Pages: 418
Release: 2008-11-28
Genre: Science
ISBN: 9781402091193

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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.