An insight into the microorganism growth prediction by means of machine learning approaches

Abstract

Microbial enhanced oil recovery (MEOR) is a well-known oil recovery method that is greatly influenced by the growth and metabolism of the microorganisms. Given the complexities and uncertainties associated with identifying the growth mechanism of microorganism, developing an approach to estimate bacterial concentration versus different factors viz. Salinity, temperature and time is still deemed a challenge. Hence, in this study, seven different machine learning methods namely Artificial Neural Network, Support Vector Machine, Decision Tree, K-nearest Neighbors, Ensemble Learning, Random Forest and Adaptive Boosting are utilized to predict bacterial cell concentration. A databank including 110 data points of bacterial cell concentration entailing the incubation time, salinity, temperature and yeast extract has been collected and used for preparation of these models. Graphical and statistical comparisons are used to analyze the performance and accuracy of each integrated model. The retrieved results revealed that the trained ensemble learning model is the most accurate method in estimating the bacterial growth with correlation coefficient and mean squared error of 0.9163 and 0.0542 on the tested dataset, respectively. Moreover, the KNN model with correlation coefficient and mean squared error of 0.6111 and 0.1192, respectively, is the worst model among the seven estimators. This model has great accuracy in training phase while it is not accurate in validation and testing phase. Due to this fact, it can be concluded that KNN model suffers from overfitting problem. In addition, the impacts of incubation time, yeast extract, temperature and salinity on bacterial cell concentration are also ascertained using sensitivity analysis. It is discerned that the temperature and yeast extract are the most and least effective factors on growth of microorganism, respectively.