Modelling Reaeration Coefficient of Stream using Regression Analytical Method - A case of Mmubete Stream, Rivers State Nigeria

Engr. Akatah B.M., Engr. Izinyon O.C., Engr. Dr. Gwarah L.S.
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Abstract

Surface water pollution is a major problem/ occurrence in the Niger Delta region of Nigeria. Mmubete stream is never an exception. Mmubete stream is significant to the people of Rivers State owing to its usefulness in terms fishing activities and domestic usage. The modelling of Mmubete stream using regression analytical method with emphasis on incorporating mixing properties of stream in stream reaeration prediction was carried out. Water samples were collected and analysed for dissolved oxygen (DO) and temperature. The hydrodynamic data (depth, velocity, surface area, kinematic viscosity and dispersion) of the stream were measured while re-aeration coefficient using empirical models developed using regression analytical approach was determined. The results revealed that the field reaeration coefficient of Mmubete stream ranges from 2.4432d-1 to 3.7568d-1 in the wet season and 0.96d-1 to 2.712d-1 in the dry season. The reaeration coefficient of the stream ranges from 1.983d-1 to 3.088d-1 using the model 1 for the prediction, 1.983d-1 to 3.5065d-1 using the model 2 and 3.0221d-1 to d-1 4.1817 using the model 3. The R2 of the models are 0.934, 0.934 and 0.998 for models 1, 2 and 3 respectively and the standard errors are 0.11135, 0.549694 and 0.022008 for model 1,2 and 3 respectively. The models developed are reliable considering the root mean square and standard error values.
用回归分析法模拟河流再生系数——以尼日利亚河流州Mmubete河为例
地表水污染是尼日利亚尼日尔三角洲地区的一个主要问题。静默流从来都不是例外。mumbete河对河流州的人民意义重大,因为它在渔业活动和家庭使用方面很有用。采用回归分析方法对Mmubete流进行建模,重点考虑了流在再生预测中的混合特性。收集水样并分析溶解氧(DO)和温度。测量了水流的水动力数据(深度、流速、表面积、运动粘度和分散度),并利用回归分析方法建立的经验模型确定了再曝气系数。结果表明:湿季柽柳流的田间再通气系数为2.4432 ~ 3.7568d-1,旱季为0.96 ~ 2.712d-1;模型1预测流的再生系数为1.983d-1 ~ 3.088d-1,模型2预测流的再生系数为1.983d-1 ~ 3.5065d-1,模型3预测流的再生系数为3.0221d-1 ~ 4.1817。模型1、模型2和模型3的R2分别为0.934、0.934和0.998,模型1、模型2和模型3的标准误差分别为0.11135、0.549694和0.022008。考虑到均方根和标准误差值,所建立的模型是可靠的。
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