Estimation of Ammonia-Nitrogen (Nh3-N) Using an Artificial Neural Networks Under Bacterial Technology

Abstract

An Artificial Neural Network (ANN) was developed to estimate the NH 3 -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 (NH 3 -N) were used as inputs for the network. The observed and the predicted NH 3 -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.