Mathematical modeling of river water temperatures
type: CreativeWork
description: "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.
abstract: "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.
author: Long, Leland Lovell
keywords: Mathematics
date published: 2016-02-10T08:00:00Z
date modified: 2026-07-24T03:19:54Z
provider: Missouri University of Science and Technology
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Missouri--Rolla