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Full Description
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.
Contents
to AI for Environmental Science.- Environmental Science Models and Artificial Intelligence.- Basic Statistics and Basic AI: Neural Networks.- Performance Measures and Uncertainty.- Decision Trees.- to Genetic Algorithms.- to Fuzzy Logic.- Missing Data Imputation Through Machine Learning Algorithms.- Applications of AI in Environmental Science.- Nonlinear Principal Component Analysis.- Neural Network Applications to Solve Forward and Inverse Problems in Atmospheric and Oceanic Satellite Remote Sensing.- Implementing a Neural Network Emulation of a Satellite Retrieval Algorithm.- Neural Network Applications to Developing Hybrid Atmospheric and Oceanic Numerical 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: A Detailed Analysis of Source Characterization.- Reinforcement Learning of Optimal Controls.- Automated Analysis of Spatial Grids.- Fuzzy Logic Applications.- Environmental Optimization: Applications of Genetic Algorithms.- Machine Learning Applications in Habitat Suitability Modeling.
