Multiscale Gaussian Process Regression-Based GLRT for Water Quality Monitoring

This paper proposes a new contaminant detection and water quality monitoring approach. Firstly, we propose an enhanced water quality modeling technique based on machine learning (e.g Gaussian process regression (GPR)) that aims at improving the proper understanding of the behavior of water distribution systems. To improve the performances of the developed water quality model even further, multiscale representation of data will be used to develop multiscale extension of these method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale GPR method, that combines the advantages of the machine learning method with those of multiscale representation, will be developed to enhance the water quality modeling performance. Secondly, technique to detect contaminant in WDN using hypothesis testing chart will be developed. Generalized likelihood ratio test (GLRT) has shown a good detection performances when compared to the classical detection charts. Then, to further enhance the performance of contaminant detection, a multiscale GPR-based exponentially weighted moving average (EWMA) GLRT (EWMA-GLRT) chart is developed. Therefore, this paper aims at enhancing the performances of contaminant monitoring using multiscale GPR-based GLRT and MSGPR-based EWMA-GLRT approaches.