AIMC Topic: Water Quality

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Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks.

Environmental research
Monitoring water quality and river ecosystems is vital for maintaining public health and environmental sustainability. Over the past decade, data-driven methods have been extensively used for river water quality modeling, including dissolved oxygen (...

Comparison of conventional and machine learning regression models for accurate prediction of selected optical active components - A case study: The Gulf of Izmit.

Marine pollution bulletin
This study hypothesizes that advanced machine learning (ML) models can more accurately predict certain critical water quality parameters in marine environments compared to conventional regression techniques. We specifically evaluated the spatio-tempo...

Hybrid deep learning based prediction for water quality of plain watershed.

Environmental research
Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain wat...

Environmental water quality prediction based on COOT-CSO-LSTM deep learning.

Environmental science and pollution research international
Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) m...

Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.

The Science of the total environment
The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate ...

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks.

Chemosphere
The Landfill plays an important role in urban development and waste disposal. However, landfill leachate may also bring more serious pollution and health risks to the surrounding groundwater environment. Compared with other areas, the area around the...

Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data.

Environmental research
Since water is an essential resource in various fields, it requires constant monitoring. Chlorophyll-a concentration is a crucial indicator of water quality and can be used to monitor water quality. In this study, we developed methods to forecast chl...

Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system.

The Science of the total environment
Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture t...

An integrated deep learning approach for modeling dissolved oxygen concentration at coastal inlets based on hydro-climatic parameters.

Journal of environmental management
Climate change has a significant impact on dissolved oxygen (DO) concentrations, particularly in coastal inlets where numerous human activities occur. Due to the various water quality (WQ), hydrological, and climatic parameters that influence this ph...

Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning.

Water research
Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollutio...