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

Rattikorn Hewett, John Leuchner, Marco Carvalho

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)292-297
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
StatePublished - 2001
Event2001 IEEE International Conference on Systems, Man and Cybernetics - Tucson, AZ, United States
Duration: Oct 7 2001Oct 10 2001

Keywords

  • Data mining
  • Induction
  • Machine learning

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