This paper investigates a machine learning approach to discovering predictive relationships that can be used to integrate solar and ocean-atmospheric conditions into forecasts of regional water flows. In particular, we apply a decision tree learning and a recently developed inductive technique called second-order table compression to generate predictive models of future water inflows of Lake Okeechobee, a primary source of water supply for south Florida. We describe SORCER, a second-order table compression learning system and compares its results with those obtained from a well-established decision tree learner, C4.5. On the average of ten 10-fold cross validations, SORCER obtained a slightly lower error rate than C4.5. We discuss the implication of these results.
|Number of pages||6|
|Journal||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|State||Published - 2001|
|Event||2001 IEEE International Conference on Systems, Man and Cybernetics - Tucson, AZ, United States|
Duration: Oct 7 2001 → Oct 10 2001
- Data mining
- Machine learning