Browsing by Author "Chimezie, Udochi Ijeoma."

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    Prediction of re-aeration coefficient of rivers from streamflow characteristics
    (Federal University of Technology, Owerri., 2023-04) Chimezie, Udochi Ijeoma.
    In this study, a new model, designated as the N-Model, was developed to predict the re-aeration coefficient of rivers using streamflow characteristics. Re-aeration is an important phenomenon that sustains dissolved oxygen levels in receiving waters to support aquatic life and natural selfpurification of streams and rivers. Development of the N-Model was done for the sole purpose of getting a more accurate model that could easily be applied with no heavy laboratory work in order to be put into application on site. The performance of the N-Model is compared against various existing empirical models like O'Connor, Parkhurst, Churchill, Krenkel, Thackston, and Owen. Data collection was done from the following rivers: Otammiri, Kaduna, Adada, Oshika lake and Atuwara. Using O'Connor's model, the re-aeration coefficient of the Otammiri River was found to be 0.0753 with a very high correlation coefficient of 99.2%. The N-Model predicted the value for Otammiri River to be 0.076 with an accuracy of 98.4%. This was determined by comparing the predicted value of the N-Model with observed data, where the minimal difference between the two (0.076 vs. 0.075) indicated that the model was highly effective in estimating the re-aeration coefficient. The model by Parkhurst produced a coefficient of 0.078 while other models like the ones by Churchill and Owen showed much higher discrepancies with percentage errors of over 70%. Across all rivers, the N-Model demonstrated strong predictive accuracy, with an overall correlation coefficient of 98.9% and a low standard error of less than 4%. However, other models like Churchill and Krenkel give very small correlation coefficients often less than 25%, indicating that the N-Model offers a reliable and efficient alternative for estimating re-aeration coefficients from streamflow characteristics in varying environments.