Discovering Hydrologic Forecasting Rules for Water Management: A Preliminary Result

Rattikorn Hewett, John Leuchner, Paul Trimble

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

This paper addresses the problem of integrating and down scaling the effects of atmospheric-oceanic global phenomena and solar variability, to enhance regional hydrologic forecasting. Our approach uses a rule set induction technique based on second-order tables (database relations in which tuples have sets of atomic values as components). In this theoretical framework, learning can be viewed as a table compression problem in which a table consisting of training set data is transformed into a second-order table with fewer rows by merging rows in consistency preserving ways. Given a data set of global climate conditions, solar variability, and inflows for Lake Okeechobee, we apply table compression to produce rules for predicting future Lake Okeechobee inflows, which can then be incorporated in an operational schedule for managing the lake. The paper presents preliminary results.

Original languageEnglish
Title of host publicationProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 1
EditorsP.P. Wang, P.P. Wang
Pages476-479
Number of pages4
Edition1
StatePublished - 2000
EventProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 - Atlantic City, NJ, United States
Duration: Feb 27 2000Mar 3 2000

Publication series

NameProceedings of the Joint Conference on Information Sciences
Number1
Volume5

Conference

ConferenceProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000
CountryUnited States
CityAtlantic City, NJ
Period02/27/0003/3/00

Keywords

  • Climate Science
  • Machine learning
  • Relational databases
  • Rule set induction

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  • Cite this

    Hewett, R., Leuchner, J., & Trimble, P. (2000). Discovering Hydrologic Forecasting Rules for Water Management: A Preliminary Result. In P. P. Wang, & P. P. Wang (Eds.), Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 1 (1 ed., pp. 476-479). (Proceedings of the Joint Conference on Information Sciences; Vol. 5, No. 1).