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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.

This book describes various AI techniques environmental scientists and engineers can use to process the vast amount of available data to enhance our understanding of Earth, including neural networks, decision trees, genetic algorithms and fuzzy logic.

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.

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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 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.

정보제공 :

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.