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Air Pollution

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An ensemble learning based hybrid model and framework for air pollution forecasting.

Environmental science and pollution research international
As advance of economy and industry, the impact of air pollution has gradually gained attention. In order to predict air quality, there were many studies that exploited various machine learning techniques to build predictive model for pollutant concen...

Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Environmental science & technology
Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with acc...

Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.

Chemosphere
The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were largely impeded in a complex system including multiple environmental stressors and health outcome of w...

Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

Sensors (Basel, Switzerland)
The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in ...

Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables.

International journal of environmental research and public health
Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO, NH, NO, NO, NO, O,...

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...

Machine Learning-Based Forecast of Hemorrhagic Stroke Healthcare Service Demand considering Air Pollution.

Journal of healthcare engineering
This study aimed to forecast the pattern of the demand for hemorrhagic stroke healthcare services based on air quality and machine learning. Hemorrhagic stroke, air quality, and meteorological data for 2016-2017 were obtained from the Longquanyi Dist...

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...

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 ...

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...