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.

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. The book is a scientific as well as a cultural blend: one culture entwines ideas with a thread, while another links them with a red line. Thus, a “red thread” ties the book together and weaves the fabric of the methods into a tapestry that pictures the ‘natural’ data-driven artificial intelligence methods in the light of the more traditional modeling techniques. The international authors, who are recognized major experts in their respective fields, bring to life ways to apply artificial intelligence to problems in the environmental sciences, demonstrating the power of these data-based methods.