Noise Removal in River Flow Forecasting

Jackson A. Criswell, En Bing Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We report a study of river flow modeling and forecast by using both noisy and thresholded discharge data as inputs to a neuro-wavelet based neural network. The data was was obtained from USGS station 04156000 Tittabawassee River at Midland, Michigan. In the neuro-wavelet network we combine wavelet analysis by using Daubechies wavelet and artificial neural networks to perform river flow forecasting of the Tittabawassee River. We obtain and compare mean squared errors, correlation coefficients, and root mean squared relative errors for three model performances. Results on the potential benefit in predictive power from denoising river flow data are presented and discussed.

Original languageEnglish
Title of host publicationProceedings of the 2021 23rd International Conference on Process Control, PC 2021
EditorsRadoslav Paulen, Miroslav Fikar
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages219-224
Number of pages6
ISBN (Electronic)9781665403306
DOIs
StatePublished - Jun 1 2021
Event23rd International Conference on Process Control, PC 2021 - Virtual, Strbske Pleso, Slovakia
Duration: Jun 1 2021Jun 4 2021

Publication series

NameProceedings of the 2021 23rd International Conference on Process Control, PC 2021

Conference

Conference23rd International Conference on Process Control, PC 2021
Country/TerritorySlovakia
CityVirtual, Strbske Pleso
Period06/1/2106/4/21

Keywords

  • Wavelet
  • denoising
  • genetic algorithm
  • multilayer perceptron
  • neural network
  • resource management

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