Enhancing Water Health Monitoring with ML Techniques for Detection of Coliform Bacteria: A Review

Abstract

Water health monitoring is critical for ensuring safe drinking water and preventing waterborne diseases. Traditional methods for detecting coliform bacteria, including culture-based techniques and biochemical tests, are well-established but face limitations such as time consumption, high costs, and labor intensity, particularly in resource-limited settings like Nigeria. Recent cholera outbreaks in Nigeria have underscored the urgent need for more effective and timely water quality monitoring solutions. This review explores the application of machine learning (ML) techniques in enhancing the detection of coliform bacteria, offering a promising alternative to traditional methods. ML approaches, including Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Ensemble Methods, are evaluated for their potential to provide faster, more accurate, and scalable detection of coliform contamination. The review highlights key challenges, such as data quality, computational demands, and infrastructure limitations, and discusses real-world case studies demonstrating the practical applications and limitations of ML techniques. The integration of ML models into water monitoring systems shows considerable promise but requires addressing critical issues related to data quality and model feasibility in low-resource settings. Future research directions include exploring hybrid systems that combine ML with traditional methods, leveraging emerging technologies like edge computing, and enhancing model robustness through innovative data strategies. By advancing the application of ML in water health monitoring, it is possible to improve public health outcomes and effectively manage waterborne diseases.