A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Incremental induction of topologically minimal trees
Proceedings of the seventh international conference (1990) on Machine learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Embracing causality in specifying the indirect effects of actions
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Causal theories of action and change
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Local maximum ozone concentration prediction using soft computing methodologies
Systems Analysis Modelling Simulation
Environmental Decision Support Systems: Guest-editorial
AI Communications
Feature selection for air quality forecasting: a genetic algorithm approach
AI Communications - Binding Environmental Sciences and Artificial Intelligence
A case study of knowledge modelling in an air pollution control decision support system
AI Communications - Binding Environmental Sciences and AI
An application of artificial neural networks in environmental pollution forecasting
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
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As alternative to physical models, neural networks are a valuable forecast tool in environmental sciences. They can be used effectively due to their learning capabilities and their low computational costs. As far as the relevant variables of the system are measured and put into the network, it works fast and accurately. However, one of the major shortcomings of neural networks is that they do not reveal causal relationships between major system components and thus are unable to improve the explicit knowledge of the user. To overcome this problem, we introduce an approach for deriving qualitative informations out of neural networks. Some of the resulting rules can be directly used by a qualitative simulator for producing possible future scenarios. Because of the explicit representation of knowledge the rules should be easier to understand and can be used as starting point for creating models wherever a physical model is not available. We illustrate our approach using a Network for predicting surface ozone concentrations and discuss open problems and future research directions.