Predictive analysis of microbial water quality using machine-learning algorithms
Keywords:machine learning, feed forward, cascade forward, layer-recurrent, regression support vector machine, Coliform bacteria, E. coli
Given the increasing recognition of machine learning tools for use in water quality monitoring, enhancing their applicability in full-scale plants require investigation of their capabilities and limitations in key aspects of the water supply chain. This study comprehensively evaluates the performances of three Artificial Neural Network (ANN) training algorithms and three solvers for regression Support Vector Machine (SVM) with different kernel functions in the estimation of the counts of Coliform bacteria from measured records of physcho-chemical water quality parameters. In addition, input data were subjected to different normalization methods to determine their effects on the performances of both ANN and SVM models. The feedforward and the cascade forward algorithms yielded the lowest MSE values among the various ANN model configurations. No distinct disparity was found in the performances of the various solvers of regression SVM in the estimations. For the regression SVM kernel functions, the Radial Basis Function (RBF) and the Gaussian kernel functions resulted in the lowest MSE values. Both the ANN and regression SVM have comparable abilities in predicting the levels of the faecal indicator organisms in the raw water. However, the ANN models were more efficient in estimating intense variations in the levels of the indicator organisms in raw water.
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