Artificial Intelligence Methods in the Environmental Sciences by Sue Ellen HauptArtificial Intelligence Methods in the Environmental Sciences by Sue Ellen Haupt

Artificial Intelligence Methods in the Environmental Sciences

bySue Ellen Haupt

Hardcover | November 26, 2008

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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.

Dr. Sue Ellen Haupt is Head of the Department of Atmospheric and Oceanic Physics at the Applied Research Laboratory of The Pennsylvania State University and Associate Professor of Meteorology. She received her Ph.D. in Atmospheric Science from the University of Michigan, M.S. in Mechanical Engineering from Worcester Polytechnic Institu...
Title:Artificial Intelligence Methods in the Environmental SciencesFormat:HardcoverDimensions:424 pagesPublished:November 26, 2008Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:1402091176

ISBN - 13:9781402091179

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Table of Contents

Preface.- Part I: Introduction To AI For Environmental Science. Overview of Using AI in Environmental Science. On "traditional" statistics and AI. On performance assessment. Decision Trees. Introduction to Genetic Algorithms. Introduction to Fuzzy Logic Algorithms. Missing Data Imputation through Machine Learning Algorithms.- Part II: Applications Of AI In Environmental Science. Nonlinear principal component analysis. Forward and Inverse Problems in Geophysical Satellite Remote Sensing: Retrieving Geophysical Parameters from Satellite Measurements and Direct Assimilation of Satellite Measurements. Neural Network Emulation of a Satellite Retrieval Algorithm. Improving Computational Efficiency of Numerical Models. Developing NN Emulations for Model Physics Parameterizations in Climate and Weather Prediction Models. Neural network modeling in climate change studies. Neural networks for characterization and forecasting in the boundary layer via radon data. Addressing Air Quality Problems with Genetic Algorithms. Reinforcement Learning for Optimal Control. Image processing techniques. Applications of Fuzzy Logic. Applications of Genetic Algorithms. Machine Learning Applications in Habitat Suitability Modeling.- Glossary. Index.