AI Medical Compendium Topic:
Environmental Monitoring

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Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery.

Environmental pollution (Barking, Essex : 1987)
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and efficient inland water qauality monitoring. Many studies have focused on evaluating and quantifying t...

Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning.

Journal of hazardous materials
Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome ...

Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms.

Environmental science and pollution research international
A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithm...

Neural networks for increased accuracy of allergenic pollen monitoring.

Scientific reports
Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in imag...

Application of artificial intelligence deep learning in numerical simulation of seawater intrusion.

Environmental science and pollution research international
Seawater intrusion not only causes fresh water shortages in coastal areas, but also has a negative impact on regional economic and social development. Global climate change will affect precipitation, sea level, and many other factors, which will in t...

A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.

Environmental monitoring and assessment
Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accurac...

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks.

PloS one
Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become...

A comparative study of deep learning-based network model and conventional method to assess beach debris standing-stock.

Marine pollution bulletin
The conventional survey of marine debris standing-stock has various drawbacks such as high cost and inaccuracy because the total amount of debris in the whole beach is inferred using the results of the manual investigation in selected narrow areas. T...

A novel multi-model data-driven ensemble approach for the prediction of particulate matter concentration.

Environmental science and pollution research international
Accuracy in the prediction of the particulate matter (PM and PM) concentration in the atmosphere is essential for both its monitoring and control. In this study, a novel neuro fuzzy ensemble (NF-E) model was proposed for prediction of hourly PM and P...

A spatially based quantile regression forest model for mapping rural land values.

Journal of environmental management
Rural land valuation plays an important role in the development of land use policies for agricultural purposes. The advance of computational software and machine learning methods has enhanced mass appraisal methodologies for modeling and predicting e...