Prediction of lake inflows with neural networks

John F. Kolen, Rattikorn Hewett

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

This paper addresses the problem of integrating the effects of climate history and solar variability, to enhance regional hydrologic forecasting using neural networks. A previous attempt at modeling the inflow to Lake Okeechobee employed a multilayered perceptron. While the resulting model was able to capture some regularities of the measured inflow, it was far from being a useful predictive model. In this paper, we continue the lake inflow modeling effort by examining data representation, quadratic input transformations, and time-delay neural networks.

Original languageEnglish
Pages (from-to)572-576
Number of pages5
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
StatePublished - 2000
Event2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: Oct 8 2000Oct 11 2000

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