AIMC Topic: Environmental Monitoring

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An artificial neural network ensemble approach to generate air pollution maps.

Environmental monitoring and assessment
The objective of this research is to propose an artificial neural network (ANN) ensemble in order to estimate the hourly NO concentration at unsampled locations. Spatial interpolation methods and linear regression models with regularization have been...

Environmental odour management by artificial neural network - A review.

Environment international
Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are c...

Using satellite-measured relative humidity for prediction of Metisa plana's population in oil palm plantations: A comparative assessment of regression and artificial neural network models.

PloS one
Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. T...

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...