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Linear Regression Analysis of the Suspended Sediment Load in Rivers and Streams Using Data of Similar Precipitation Values

Jamison, Jonathan A
http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279

Abstract Details

2018, Master of Science in Environmental Science, Youngstown State University, Department of Physics, Astronomy, Geology and Environmental Sciences.
Sediment provides a method for transportation of a variety of other pollutants such as nutrients and potentially harmful bacteria. In addition, sediment can increase the cost of water treatment processes and reduce storage volume of water reservoirs. This study employs linear regression to predict the annual suspended sediment load, a dependent variable, as a function of the annual river water discharge, an independent variable in four United States Rivers. The available data (annual suspended sediment load and annual river water discharge) for each river was broken down into groups based upon similar precipitation values. Each river was divided into two or three groups, with a total of ten groups for the four rivers. Linear regression was applied to each group. Results of the precipitation approach were compared to those of the traditional approach, the latter did not use any precipitation data and thus there is no individual groupings. The precipitation approach provided higher accuracy for the prediction of the suspended sediment load when compared to the traditional approach. The prediction accuracy is evident from the high correlation coefficient values (between the suspended sediment and river water discharge), and the low percent deviations (percent difference between the observed and predicted suspended sediment). Of the ten river groups, seven resulted in higher correlation coefficients, and five gave lower percent deviations compared to the traditional approach. The mean percent deviation ranged between 20 and 26% in seven groups, which is considered an indication of high accuracy when suspended sediment is predicted by linear regression. All of the ten groups resulted in higher correlation coefficient values greater or equal to 0.80, with four groups exceeding 0.90.
Isam Amin, PhD (Committee Chair)
Alan Jacobs, PhD (Committee Member)
Colleen McLean, PhD (Committee Member)
Anna Draa, PhD (Committee Member)
83 p.
Environmental Science; Geomorphology
linear regression

Recommended Citations

Citations

  • Jamison, J. A. (2018). Linear Regression Analysis of the Suspended Sediment Load in Rivers and Streams Using Data of Similar Precipitation Values [Master's thesis, Youngstown State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279

    APA Style (7th edition)

  • Jamison, Jonathan. Linear Regression Analysis of the Suspended Sediment Load in Rivers and Streams Using Data of Similar Precipitation Values . 2018. Youngstown State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279.

    MLA Style (8th edition)

  • Jamison, Jonathan. "Linear Regression Analysis of the Suspended Sediment Load in Rivers and Streams Using Data of Similar Precipitation Values ." Master's thesis, Youngstown State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ysu154273822580279

    Chicago Manual of Style (17th edition)

ysu154273822580279
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This open access ETD is published by Youngstown State University and OhioLINK.