Escherichia coli growth modeling using neural network

Abstract

The assessment of water microbial quality is normally performed by verification of Escherichia coli where the growth is in nonlinearity. NARX is computational tools that have extensive utilization in solving nonlinear time series problems. It is well known technique that has the ability to predict with efficient and good performance. Using NARX, a highly accurate model was developed to predict the growth of Escherichia coli (E. coli) based on pH water parameter. The multiparameter portable sen used to build and train the neural network. The selection of neural network structure for pH and optical density modelling was optimized and also the training and validation were analyzed. The result exhibited that NARX based on pH water parameter with overall regression is 0.99956. The assessment of water microbial quality is normally performed by verification of Escherichia coli where the growth is in nonlinearity. NARX is computational tools that have extensive utilization in solving nonlinear time series problems. It is well known as one of the technique that has the ability to predict with efficient and good performance. Using NARX, a highly accurate model was developed to predict the growth of Escherichia coli (E. coli) based sor and spectrophotometer data were used to build and train the neural network. The selection of neural network structure for pH and optical density modelling was optimized and also the training and validation were modeling was able to predict the growth of E. coli neural network; NARX; prediction; Escherichia coli; pH; optical density. Keywords