Superposition learning-based model for prediction of E.coli in groundwater using physico-chemical water quality parameters

Abstract

The prediction of waterborne bacteria is crucial to prevent health risks. Therefore, there is a need to study the quality of groundwater by predicting the presence of E.coli. The experimental data for prediction is obtained from BITS-UVA (University of Virginia) groundwater contamination project, having 1301 experimental laboratory results that will be synthesized to test the physical, chemical, and microbiological parameters of water. In this study, a superposition-based learning algorithm (SLA) is proposed to observe the patterns of ANN-based sensitivity analysis to determine the importance of each water quality parameter resulting in the prediction of E.coli in groundwater. Mean Square Error (MSE) and the Coefficient of determination (R2) are calculated using MATLAB (R2019b, Mathworks, Natik, MA, USA) software for model performance evaluation. The highest correlation is observed between E.coli and the pH values, whereas the lowest correlation is observed with Dissolved Oxygen. In order to find out the uncertainty in the output of our mathematical model, a sensitivity analysis for the seven models is carried out. The results show that the model having Turbidity, pH, Total Dissolved Salts (TDS), and Electrical Conductivity as inputs displayed the best performance. The model architecture constitutes three hidden layers, twenty neurons in each layer, which is optimized using the Bayesian Regularization training algorithm (BR) with the overall highest R2 values of 0.90 and lowest MSE values of 0.0892. Patterns of the trained neural network are presented in superposition. After training, it can be concluded that the superposition models based on Grover's algorithm is more efficient in predicting all patterns in the counts of E.coli in groundwater. The algorithm is superimposed on multiple neural network architectures and returns a trained neural network. In addition to accurate results, there is also a need to automate the process of real-time bacterial monitoring for minimizing the error.