Deriving qualitative rules from neural networks – a case study for ozone forecasting

Abstract

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.