Abstract

Gebdang Biangbalbe Ruben^1*, Xie Yuebo², and Andam-Akorful A. Samuel³

DOI : http://dx.doi.org/10.13005/ojc/320104

An Artificial Neural Network (ANN) was developed to estimate the NH₃-N under the bacterial technology in Xuxi River, China. Eight water quality variables such as Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), Total Nitrogen (TN), Total Phosphorus (TP), Suspended Sediment (SS), Temperature, Transparency, and Ammonia Nitrogen (NH₃-N) were used as inputs for the network. The observed and the predicted NH₃-N of the trained networks showed a good fit after the training with a coefficient of correlation (r) and a root mean square error (RMSE) of 0.91 and 2.61 respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable; TN, Transparency, DO, and TP have proven to be the most effective inputs. Their training’s results showed a coefficient of correlation (r = 0.9295) and a (RMSE = 1.2081) which is more accurate than the prediction with eight inputs variables.

Artificial Neural Networks; bacterial technology; ammonia nitrogen; estimation; Xuxi River

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