A comparison between the multiple linear regression model and neural networks for biochemical oxygen demand estimations

The most common test for determining the strength of organic content in wastewaters is the biochemical oxygen demand (BOD). The variables of water quality are temperature, pH value (pH), dissolved oxygen (DO), substance solid (SS), total Kjeldahl nitrogen (TKN), ammonia nitrogen (NH₃N), nitrate (NO₃), total phosphorous(T-P), and total coliform bacteria (T-coliform). These water quality indices affect biochemical oxygen demand. The main objective of this study was to compare between the predictive ability of the neural network (NN) models and the multiple linear regression (MLR) models to estimate the biochemical oxygen demand on data from 288 canals in Bangkok, Thailand. The data were obtained from the department of drainage and sewerage, Bangkok metropolitan administration, during 2002-2008. The results showed that the neural network models gave a higher correlation coefficient (R=0.76) and a lower mean square error (MSE=0.0016) than the corresponding multiple linear regression models.