AIMC Topic: Water Quality

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Dynamic patterns and potential drivers of river water quality in a coastal city: Insights from a machine-learning-based framework and water management.

Journal of environmental management
River water quality continues to deteriorate under the coupled effects of climate change and human activities. Machine learning (ML) is a promising approach for analyzing water quality. Nevertheless, the spatiotemporal dynamics of river water quality...

Leveraging explainable machine learning for enhanced management of lake water quality.

Journal of environmental management
Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km) subtropical lake as a...

Prediction of urban surface water quality scenarios using hybrid stacking ensembles machine learning model in Howrah Municipal Corporation, West Bengal.

Journal of environmental management
In the pursuit of understanding surface water quality for sustainable urban management, we created a machine learning modeling framework that utilized Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splin...

Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir.

Environmental research
The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal bl...

Hypoxia extreme events in a changing climate: Machine learning methods and deterministic simulations for future scenarios development in the Venice Lagoon.

Marine pollution bulletin
Climate change pressures include the dissolved oxygen decline that in lagoon ecosystems can lead to hypoxia, i.e. low dissolved oxygen concentrations, which have consequences to ecosystem functioning including biogeochemical cycling from mild to seve...

Development of a machine learning model to support low cost real-time Legionella monitoring in premise plumbing systems.

Water research
Legionella pneumophila (L. pneumophila) is a pathogenic bacterium primarily known for causing Legionnaires' Disease which is known for high mortality rates, particularly in the elderly. With caseloads continuing to increase, further research is neede...

Application of remote sensing technology in water quality monitoring: From traditional approaches to artificial intelligence.

Water research
Quantitative estimation is a key and challenging issue in water quality monitoring. Remote sensing technology has increasingly demonstrated its potential to address these challenges. Remote sensing imagery, combined with retrieval algorithms such as ...

Classifying eutrophication spatio-temporal dynamics in river systems using deep learning technique.

The Science of the total environment
Eutrophication is a major cause of water quality degradation in South Korea, owing to severe algal blooms. To manage eutrophication, the South Korean government provided the Trophic State Index (TSIko), which was revised according to Carlson's TSI. T...

Groundwater quality prediction and risk assessment in Kerala, India: A machine-learning approach.

Journal of environmental management
Despite its critical importance for health, agriculture, and the economy, and its key role in supporting climate change adaptation, groundwater quality remains vulnerable to contamination and is often neglected until significant deterioration. The gr...

Integrating deep learning techniques for effective river water quality monitoring and management.

Journal of environmental management
Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality param...