Dissolved Oxygen Estimation using Artificial Neural Network for Water Quality Control

Dissolved oxygen (DO) is one of the key parameters when analysing the river water quality. Correct estamition of DO being carried by a river is very important for water quality control. DO is affected by lots of variables such as Biochemical Oxygen Demand (BOD), nitrification, reaeration, sedimentation, photosynthesis, water discharge and temperature for that reason it is hard to solve like a complex problem. The methods available in the literature for DO estimation are complicated time consuming and neccesitate cumbersome parameter estimation procedures. Artificial Neural Networks (ANNs) are a simplied mathematical representations of the functioning of the human brain. This paper examins the potential of ANN in estimating the DO from limited data (NO2-N, NO3-N, BOD, water discharge and temperature). This study employed feed forward (FF) type ANN for computing monthly values of DO. The results of the study clearly demonstrate the ANN results are very close to the observed values of DO.