From climate history to prediction of regional water flows with machine learning

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 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, USA. We describe SORCER (Second-Order Relation Compression for Extraction of Rules), which is a 2nd-order table compression learning system, and compare its results with those obtained from a well-established decision tree learner, C4.5. On the average, in ten 10-fold cross-validations, SORCER obtained a slightly lower error rate than C4.5. We discuss the implications of these results