Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

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A New Hybrid Model FPA-SVM Considering Cointegration for Particular Matter Concentration Forecasting: A Case Study of Kunming and Yuxi, China.Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

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P2860

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

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2016 nî lūn-bûn

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2016年の論文

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Prediction of air pollutant co ...... g feedforward neural networks.

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P1476

Prediction of air pollutant co ...... g feedforward neural networks.

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P2093

Jiangshe Zhang

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Weifu Ding

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Yee Leung

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P2860

NO2, as a marker of air pollution, and recurrent wheezing in children: a nested case-control study within the BAMSE birth cohort

Land use regression modeling to estimate historic (1962-1991) concentrations of black smoke and sulfur dioxide for Great Britain.

Hierarchical Bayesian inference in the visual cortex.

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean.

A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments.

Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment.

Forecasting hourly PM(10) concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management.

An online air pollution forecasting system using neural networks.

Applying land use regression model to estimate spatial variation of PM₂.₅ in Beijing, China.

Spatial modeling of PM2.5 concentrations with a multifactoral radial basis function neural network.

P2888

s11356-016-7149-4

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19481-19494

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scholarly article

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10.1007/S11356-016-7149-4

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English

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2016-07-06T00:00:00Z

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27384165

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Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

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P2860

P2860

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

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