AI Medical Compendium Journal:
Environmental pollution (Barking, Essex : 1987)

Showing 51 to 60 of 106 articles

Application of improved machine learning in large-scale investigation of plastic waste distribution in tourism Intensive artificial coastlines.

Environmental pollution (Barking, Essex : 1987)
Oceans are ultimately a sink of plastic waste. Complex artificial coastlines pose remarkable challenges for coastal plastic waste monitoring. With the development of machine learning methods, high detection accuracy can be achieved; however, many fal...

Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems.

Environmental pollution (Barking, Essex : 1987)
Water quality index (WQI) is a well-established tool for assessing the overall quality of fresh inland-waters. However, the effectiveness of real-time assessment of aquatic ecosystems using the WQI is usually impacted by the absence of some water qua...

Is replacing missing values of PM constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

Environmental pollution (Barking, Essex : 1987)
East Asian countries have been conducting source apportionment of fine particulate matter (PM) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Ko...

Development and application of machine learning models for prediction of soil available cadmium based on soil properties and climate features.

Environmental pollution (Barking, Essex : 1987)
Identifying the key influencing factors in soil available cadmium (Cd) is crucial for preventing the Cd accumulation in the food chain. However, current experimental methods and traditional prediction models for assessing available Cd are time-consum...

Modeling risk assessment of soil heavy metal pollution using partial least squares and fuzzy logic: A case study of a gully type coal-based solid waste dumpsite.

Environmental pollution (Barking, Essex : 1987)
Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and r...

Integrating advanced techniques and machine learning for landfill leachate treatment: Addressing limitations and environmental concerns.

Environmental pollution (Barking, Essex : 1987)
This review article explores the challenges associated with landfill leachate resulting from the increasing disposal of municipal solid waste in landfills and open areas. The composition of landfill leachate includes antibiotics (0.001-100 μg), heavy...

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.

Environmental pollution (Barking, Essex : 1987)
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such a...

Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning.

Environmental pollution (Barking, Essex : 1987)
Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there r...

Explainable geospatial-artificial intelligence models for the estimation of PM concentration variation during commuting rush hours in Taiwan.

Environmental pollution (Barking, Essex : 1987)
PM concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM concentration and its spatial distribution during rush hours using machine learning mo...

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction.

Environmental pollution (Barking, Essex : 1987)
The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine l...