Improved Estimates for the Accuracy of Small Disjuncts
Machine Learning
Using the m-estimate in rule induction
Journal of Computing and Information Technology
Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
A framework for linguistic modelling
Artificial Intelligence
Modelling and Reasoning with Vague Concepts (Studies in Computational Intelligence)
Modelling and Reasoning with Vague Concepts (Studies in Computational Intelligence)
Expert Systems with Applications: An International Journal
Decision tree learning with fuzzy labels
Information Sciences: an International Journal
Fuzzy neural networks for water level and discharge forecasting with uncertainty
Environmental Modelling & Software
Prediction of ocean wave energy from meteorological variables by fuzzy logic modeling
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain
IEEE Transactions on Fuzzy Systems
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A linguistic decision tree algorithm (LID3) is applied to the problem of predicting storm surge. Of particular interest is the prediction of large positive storm surge for flood warning purposes. The application site is the North Sea which has a well-understood physical system for the generation and progression of storm surge, which lends itself to testing of the LID3 algorithm on a real-world prediction problem. Using available water level and meteorological data, the decision tree provides predictions of surge on the Thames Estuary up to 8h in advance, accurate to the order of 0.1m, which is comparable to alternative data driven methods. However, the success of the data driven approaches applied here are all limited by the sparsity of training data for extreme events (which by their nature are rare). A major benefit of the decision tree approach is the ability to make inference from the resulting IF-THEN rules of the tree structure. In this application of the LID3 algorithm, clear and plausible model rules can be deduced from the tree structure that are consistent with our understanding of the physical drivers of storm surge at this location. The label semantic framework is interpreted probabilistically, allowing the user to employ standard statistical approaches to identify statistically significant rules. It is demonstrated that the rules can successfully discriminate between surges that may pose a threat and those that should not, based on tide gauge measurements available up to 8h prior to the surge signal reaching the Thames Estuary. This is promising for the potential application of such computationally efficient and easy to implement rule learning algorithms for the further investigation of complex environmental systems.