AIMC Topic: Water Microbiology

Clear Filters Showing 1 to 10 of 39 articles

Adversarial susceptibility analysis for water quality prediction models.

Scientific reports
Water quality is a critical factor for human health and environmental sustainability. Rapid urbanization and industrialization have led to significant water contamination, increasing the prevalence of waterborne diseases. This study investigates the ...

Machine Learning, Generalization, and Transfer Learning for Predicting the Exceedance of Fecal Indicator Bacteria Thresholds at Beaches.

Environmental science & technology
Beach water testing for fecal indicator bacteria (FIB) is a key element of public health protection for beachgoers. Because the process can be expensive and time-consuming, many beaches are infrequently monitored, putting the health of the public at ...

Urbanization intensifies deterministic selection of pathogenic bacteria in river networks: Nitrogen-driven niche partitioning and cross-scale risk forecasting through DOM-bacteria interplay.

Environmental research
Urbanization modifies the composition of dissolved organic matter (DOM) and nitrogen nutrients, profoundly affecting river microbial communities. However, the mechanisms driving pathogenic and non-pathogenic bacteria remain unclear. In this study, we...

Hydraulic and thermal performance trigger the deterministic assembly of water microbiomes: From biogeographical homogenization to machine learning model.

Water research
Water quality at the point of consumption has long been a health issue because of the potential for microbial ecology. However, research on water hydraulic performance remains in its infancy, and in particular, little is known about the effects of th...

An investigation of microbial groundwater contamination seasonality and extreme weather event interruptions using "big data", time-series analyses, and unsupervised machine learning.

Environmental pollution (Barking, Essex : 1987)
Temporal studies of groundwater potability have historically focused on E. coli detection rates, with non-E. coli coliforms (NEC) and microbial concentrations remaining understudied by comparison. Additionally, "big data" (i.e., large, diverse datase...

A calibration framework toward model generalization for bacteria concentration estimation in water resource recovery facilities.

Scientific reports
Reduced bacteria concentrations in wastewater is a key indicator of the efficacy of water resource recovery facilities (WRRFs). However, monitoring the presence of bacterial concentrations in real time at each stage of the WRRF is challenging as it r...

Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Environment international
Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts...

Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water.

ACS applied materials & interfaces
The challenge of rapid identification of bacteria in food and water still persists as a major health problem. To tackle this matter, we have developed a single-probe liquid crystal (LC)-based optical sensing platform for the differentiation of five c...

Integrating microbial profiling and machine learning for inference of drowning sites: a forensic investigation in the Northwest River.

Microbiology spectrum
Drowning incidents present significant challenges for forensic investigators in determining the exact site of occurrence. Traditional forensic methods often rely on physical evidence and circumstantial clues, but the emerging field of forensic microb...

Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South Korea.

The Science of the total environment
This study addresses the critical public health issue of fecal coliform contamination in the four major rivers in South Korea (Han, Nakdong, Geum, and Yeongsan rivers) by applying advanced machine learning (ML) algorithms combined with Explainable Ar...