Real-time flood forecasting using updateable linguistic decision trees.

This paper focuses on the application of LID3 (linguistic decision tree induction algorithm) to real-time flood forecasting. Specifically the prediction of the river level at locations along the River Severn, Britainpsilas largest river. Modelling river dynamics implies modelling a system that changes over time. It is therefore inappropriate to use a static model to model river levels, that are driven by an underlying dynamic system. Hence, an updateable version of LID3 is proposed. There are two main features of ULID3 (updateable LID3). The first being error-based updating, which weights new instances depending on the treepsilas current ability to describe each new example. The ability to update probability distributions at each node enables the tree to adapt and capture the new dynamic concept more effectively. The second feature is the ability to extend both the input and output domains, given new examples. This is necessary when the data available for updating, exceeds the current domain set by the training data. An algorithm is presented to update the new probability distributions throughout the tree, without the need for storing the complete set of examples at each node.