Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Machine Learning Methods for Ecological Applications
Machine Learning Methods for Ecological Applications
Applications of Neural Networks in Environment, Energy, and Health: Proceedings of the 1995 Workshop on Environmental and Energy Applications of Neural Networks, Pacific Northwest National Laboratory, Richland, Washington, U. S. A., 30-31 Mar
Predicting Chemical Parameters of River Water Quality from Bioindicator Data
Applied Intelligence
Guest Editorial: Statistical Mining and Data Visualization in Atmospheric Sciences
Data Mining and Knowledge Discovery
Guest Editors' Introduction: Environmental Applications of AI
IEEE Expert: Intelligent Systems and Their Applications
Intelligent Retrieval of Archived Meteorological Data
IEEE Expert: Intelligent Systems and Their Applications
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Simultaneous Prediction of Mulriple Chemical Parameters of River Water Quality with TILDE
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Applications of machine learning: matching problems to tasks and methods
The Knowledge Engineering Review
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Environmental sciences are concerned with the physical, chemical, and biological aspects of the environment. They cover an extremely broad range of topics, such as biodiversity, climate change, forestry, and freshwater ecology, and are relevant to practical issues of environmental management. In this article, we attempt to give an overview of knowledge discovery in databases (KDD) applications in environmental sciences, complemented with a sample of case studies. The latter are described in slightly more detail and used to illustrate KDD-related issues that arise in environmental applications. The application domains addressed range from ecological modeling to remote sensing.