Artificial Intelligence Methods in the Environmental Sciences / Edition 1

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Overview

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 techniques, including: neural networks, decision trees, genetic algorithms, 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.
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Product Details

  • ISBN-13: 9781402091186
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 12/28/2008
  • Edition description: 2009
  • Edition number: 1
  • Pages: 432
  • Product dimensions: 7.60 (w) x 10.20 (h) x 0.80 (d)

Meet the Author

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 Institute and B.S. in Meteorology from Penn State. In addition to PSU, she has worked at New England Electric System, the National Center for Atmospheric Research, University of Colorado/Boulder, University of Nevada, Reno, and Utah State University. Her research emphasizes applying novel numerical techniques to environmental and fluid dynamics problems.

Dr. Antonello Pasini is a senior researcher at the Institute of Atmospheric Pollution of the National Research Council in Rome, Italy. He received his Italian Laurea in Physics from University of Bologna and specialized in atmospheric physics and meteorology at the Italian Met Service according to WMO criteria. He is an expert of complex systems and neural network modelling and applies his studies to several environmental problems, with a particular emphasis to climate change applications.

Dr. Caren Marzban is a senior physicist at the Applied Physics Laboratory, and an instructor at the Department of Statistics, University of Washington. He received his Ph.D. in theoretical physics from the University of North Carolina, at Chapel Hill. The early segment of his research career was in quantum gravity and string theory, but then he saw the light and began learning and applying statistics and machine learning techniques to any problem he can get his hands on.

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Table of Contents

Part I Introduction to AI for Environmental Science

1 Environmental Science Models and Artificial Intelligence Sue Ellen Haupt Valliappa Lakshmanan Caren Marzban Antonello Pasini John K. Williams 3

2 Basic Statistics and Basic AI: Neural Networks Caren Marzban 15

3 Performance Measures and Uncertainty Caren Marzban 49

4 Decision Trees G. R. Dattatreya 77

5 Introduction to Genetic Algorithms Sue Ellen Haupt 103

6 Introduction to Fuzzy Logic John K. Williams 127

7 Missing Data Imputation Through Machine Learning Algorithms Michael B. Richman Theodore B. Trafalis Indra Adrianto 153

Part II Applications of AI in Environmental Science

8 Nonlinear Principal Component Analysis William W. Hsieh 173

9 Neural Network Applications to Solve Forward and Inverse Problems in Atmospheric and Oceanic Satellite Remote Sensing Vladimir M. Krasnopolsky 191

10 Implementing a Neural Network Emulation of a Satellite Retrieval Algorithm George S. Young 207

11 Neural Network Applications to Developing Hybrid Atmospheric and Oceanic Numerical Models Vladimir M. Krasnopolsky 217

12 Neural Network Modeling in Climate Change Studies Antonello Pasini 235

13 Neural Networks for Characterization and Forecasting in the Boundary Layer via Radon Data Antonello Pasini 255

14 Addressing Air Quality Problems with Genetic Algorithms: A Detailed Analysis of Source Characterization Sue Ellen Haupt Christopher T. Allen George S. Young 269

15 Reinforcement Learning of Optimal Controls John K. Williams 297

16 Automated Analysis of Spatial Grids Valliappa Lakshmanan 329

17 Fuzzy Logic Applications John K. Williams Cathy Kessinger Jennifer Abernethy Scott Ellis 347

18 EnvironmentalOptimization: Applications of Genetic Algorithms Sue Ellen Haupt 379

19 Machine Learning Applications in Habitat Suitability Modeling Saso Dzeroski 397

Glossary 413

Index 421

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