@inproceedings{4551049ae7d94db8ba425486bec871a6,
title = "Noise Removal in River Flow Forecasting",
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.",
keywords = "Wavelet, denoising, genetic algorithm, multilayer perceptron, neural network, resource management",
author = "Criswell, {Jackson A.} and Lin, {En Bing}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 23rd International Conference on Process Control, PC 2021 ; Conference date: 01-06-2021 Through 04-06-2021",
year = "2021",
month = jun,
day = "1",
doi = "10.1109/PC52310.2021.9447523",
language = "English",
series = "Proceedings of the 2021 23rd International Conference on Process Control, PC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "219--224",
editor = "Radoslav Paulen and Miroslav Fikar",
booktitle = "Proceedings of the 2021 23rd International Conference on Process Control, PC 2021",
}