Machine Learning Approach to Detect Hazardous Algal Blooms and Fecal Indicator Bacteria

What is it about?

Based on a systematic review of convolutional neural networks (CNN), this study explores the efficacy of small imaging sensors in monitoring the real-time presence of cyanotoxins and hazardous contaminants in urban ecosystems. To develop a machine learning-based CNN, this study first investigated the relationships between the prevalence of hazardous algal blooms (HABs) and faecal indicator bacteria (FIB) in waterways and aquifers of certain semi-arid zones of Sri Lanka, Sweden and New York (United States). By incorporating a popularly known AbspectroscoPY framework to effectively process the spectrophotometric data of the obtained samples, the formulation subsequently reveals strong positive correlations between FIB coliforms and nutrient loads (particularly nitrate and phosphate). A corroborative association with the incidence of chronic kidney disease of uncertain aetiology (CKDu) among the residents of the studied regions further affirms the reliability of the methodology. These findings underline the need for policymakers to consider the geographical and land-use traits of urban habitats in strategies aimed at reducing waterborne health hazards.

Featured Image

Photo by Ivan Bandura on Unsplash

Photo by Ivan Bandura on Unsplash

Why is it important?

This review , grounded in a comprehensive analysis of convolutional neural networks (CNN), examines the effectiveness of compact imaging sensors in detecting cyanotoxins and harmful pollutants in real-time within urban ecosystems. The study initially probed the connections between the occurrence of hazardous algal blooms (HABs) and faecal indicator bacteria (FIB) in water systems and aquifers across specific semi-arid regions in Sri Lanka, Sweden and New York (United States) to construct a machine learning-based CNN. Utilising the widely recognised AbspectroscoPY framework to efficiently analyse spectrophotometric data from collected samples, the research subsequently identified robust positive correlations between FIB coliforms and nutrient concentrations (notably nitrate and phosphate). The methodology's credibility was further substantiated by a corroborative link with the prevalence of chronic kidney disease of uncertain aetiology (CKDu) amongst inhabitants of the studied areas. These outcomes emphasise the importance for policymakers to factor in the geographical and land-use characteristics of urban environments when devising strategies to mitigate waterborne health risks.

Perspectives