AI Medical Compendium Topic:
Environmental Monitoring

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Artificial neural network model to predict transport parameters of reactive solutes from basic soil properties.

Environmental pollution (Barking, Essex : 1987)
Measurement of solute-transport parameters through soils for a wide range of solute- and soil-types is time-consuming, laborious, expensive and practically impossible. So, indirect methods for estimating the transport parameters by pedo-transfer func...

Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance.

Neural networks : the official journal of the International Neural Network Society
In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the C...

Rummaging through the bin: Modelling marine litter distribution using Artificial Neural Networks.

Marine pollution bulletin
Marine litter has significant ecological, social and economic impacts, ultimately raising welfare and conservation concerns. Assessing marine litter hotspots or inferring potential areas of accumulation are challenging topics of marine research. Neve...

Downscaling satellite soil moisture using geomorphometry and machine learning.

PloS one
Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large ar...

A Novel Air Quality Early-Warning System Based on Artificial Intelligence.

International journal of environmental research and public health
The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and ...

Robotic direct reading device with spatial, temporal, and PID sensors for laboratory VOC exposure assessment.

Journal of occupational and environmental hygiene
This study evaluated a novel robotic direct reading method that used a real-time location system to measure the spatial-concentration distribution of volatile organic compounds (VOCs) in a chemistry laboratory. The CEMWIP II is a custom-made sensor t...

Prediction of environmental effects in received signal strength in FM/TV station based on meteorological parameters using artificial neural network and data mining.

Journal of environmental management
In this paper, meteorological parameters, electric field strength and transmitters' output power measured during six months in a TV/FM station. There are 13 frequencies in FM and UHF frequency bands in pilot broadcast station. The analysis of results...

Valuation methodology of laminar erosion potential using fuzzy inference systems in a Brazilian savanna.

Environmental monitoring and assessment
This study presents an approach on the evaluation of potential laminar erosion in the Ribeirão Sucuri Grande watershed. It is located in the northeast of the state of Goiás, Brazil, a conservation area under strong anthropogenic pressure. A Mamdani f...

Forecasting of bioaerosol concentration by a Back Propagation neural network model.

The Science of the total environment
Bioaerosol in the atmosphere plays a very important role in environment and public health. To forecast the bioaerosol concentration, the correlation between bioaerosol concentration and meteorological factors was discussed, and a Back Propagation (BP...

Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes.

Environmental science and pollution research international
Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), total nitrogen (TN), turbidity (TB), and Secchi depth (SD) collected by the United States Environmental Protection Agency...