Abstract
Model selection is finding wide applications in a lot of modelling and environmental problems. However, applications of model selection to re-aeration coefficient studies are still limited. The current study explores the use of model selection in re-aeration coefficient studies by combining several suggestions from numerous authors on the interpretation of data regarding re-aeration coefficient modelling. The model selection procedure applied in this research made use of Akaike information criteria, measures of agreement such as percent bias (PBIAS), Nash-Sutcliffe Efficiency (NSE) and root mean square error (RMSE) observation Standard deviation Ratio (RSR) and gragh analysis in selecting the best performing model. An algorithm prescribing a generic model selection procedure was also provided. Out of ten candidates models used in this study, the O’Connor and Dobbins (1958) model emerged as the top performing model in its application to data collected from River Atuwara in Nigeria. The suggested process could save software and model developers lots of time and resources, which would otherwise be spent in investigating and developing new models. The procedure is also ideal in selecting a model in situations where there is no overwhelming support for any particular model by observed data.
Highlights
Reaeration coefficient (k2) modelling, as a relatively new and specialized field of study, has evolved over a period of ninety years through contributions by researchers from different parts of the world (Palumbo & Brown, 2013; Omole, 2012; Gayawan et al, 2009; Ye at al., 2008; Longe & Omole, 2008)
The procedure for model selection procedure used in this paper was based on a combination of suggestions by different authors on the subject
The study suggested a procedure that used statistical tools and graphical tools to rank the capacity of ten different models to predict observed stream data (Appendix)
Summary
Reaeration coefficient (k2) modelling, as a relatively new and specialized field of study, has evolved over a period of ninety years through contributions by researchers from different parts of the world (Palumbo & Brown, 2013; Omole, 2012; Gayawan et al, 2009; Ye at al., 2008; Longe & Omole, 2008). This has resulted in the development of hundreds of k2 models, often through processes that cost large sums of money, labour and time (Wang et al, 2013). (Bowie et al, 1985; O’Connor & Dobbins, 1958) (Agunwanmba et al., 2007) (Jha et al, 2001)
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