Mathematical modeling of river water temperatures

<p>"The applicability of power spectral density techniques, Fourier series analysis, and linear regression to the mathematical modeling of river water temperature is demonstrated. Consideration is also given to the problem of estimating thermal inputs to rivers from man-made sources such as electrical power plants. First, power spectral density techniques are used in the time-series analysis of water temperature records which were taken from the Missouri River. Two spectral ranges are then studied from the standpoint of their applicability to (1) mathematical model building and (2) detection and identification of cyclic thermal inputs. Next, a Fourier regression fit to the time-series data is used to show that normal random variates having zero mean are obtained when the regression curve is extracted from the data. A 60-day prediction of daily-average water temperature is then made using a model which is based upon a polynomial regression fit to the fluctuating amplitudes of significant Fourier components. A final predictive model, which is based on the above analysis methods, is proposed"--Abstract, page ii.</p>

Name thesis:degree_name

Ph. D. in Mathematics

Grantor

University of Missouri--Rolla

Year dc:date.available

2016

Author dc:creator

• Long, Leland Lovell

• Mathematics

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/2086

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-3088