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A real-time hourly ozone prediction system using deep convolutional neural network

Neural computing & applications, 2020-07, Vol.32 (13), p.8783-8797

2020

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A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Scientific reports, 2021-05, Vol.11 (1), p.10891-10891, Article 10891

2021

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Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms

Neural computing & applications, 2021-11, Vol.33 (22), p.15073-15089

2021

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Real-time 7-day forecast of pollen counts using a deep convolutional neural network

Neural computing & applications, 2020-08, Vol.32 (15), p.11827-11836

2020

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A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks

Neural computing & applications, 2020-06, Vol.32 (11), p.7563-7579

2020

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Review of agricultural biomass burning and its impact on air quality in the continental United States of America

Environmental advances, 2024-07, Vol.16, p.100546, Article 100546

2024

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Hourly and Daily PM2.5 Estimations Using MERRA‐2: A Machine Learning Approach

Earth and space science (Hoboken, N.J.), 2022-11, Vol.9 (11), p.n/a

2022

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

Neural networks, 2020-01, Vol.121, p.396-408

2020

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CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks

Atmospheric environment (1994), 2022-03, Vol.273, p.118961, Article 118961

2022

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Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks

Atmospheric environment (1994), 2021-05, Vol.253, p.118376, Article 118376

2021

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Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach

Atmospheric environment (1994), 2021-02, Vol.247, p.118209, Article 118209

2021

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A Deep Convolutional Neural Network Model for Improving WRF Simulations

IEEE transaction on neural networks and learning systems, 2023-02, Vol.34 (2), p.750-760

2023

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Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data

Geophysical research letters, 2021-08, Vol.48 (15), p.n/a

2021

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Deep-BCSI: A deep learning-based framework for bias correction and spatial imputation of PM2.5 concentrations in South Korea

Atmospheric research, 2024-05, Vol.301, p.107283, Article 107283

2024

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Deep learning solver for solving advection–diffusion equation in comparison to finite difference methods

Communications in nonlinear science & numerical simulation, 2022-12, Vol.115, p.106780, Article 106780

2022

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The Impact of Springtime‐Transported Air Pollutants on Local Air Quality With Satellite‐Constrained NOx Emission Adjustments Over East Asia

Journal of geophysical research. Atmospheres, 2022-03, Vol.127 (5), p.n/a

2022

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Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system

Geoscientific Model Development, 2020-12, Vol.13 (12), p.6237-6251

2020

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Hourly and Daily PM 2.5 Estimations Using MERRA‐2: A Machine Learning Approach

Earth and space science (Hoboken, N.J.), 2022-11, Vol.9 (11)

2022

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The Impact of Springtime‐Transported Air Pollutants on Local Air Quality With Satellite‐Constrained NO x Emission Adjustments Over East Asia

Journal of geophysical research. Atmospheres, 2022-03, Vol.127 (5)

2022

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A real-time hourly ozone prediction system using deep convolutional neural network

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms

Real-time 7-day forecast of pollen counts using a deep convolutional neural network

A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks

Review of agricultural biomass burning and its impact on air quality in the continental United States of America

Hourly and Daily PM2.5 Estimations Using MERRA‐2: A Machine Learning Approach

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

CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks

Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks

Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach

A Deep Convolutional Neural Network Model for Improving WRF Simulations

Application of a Partial Convolutional Neural Network for Estimating Geostationary Aerosol Optical Depth Data

Deep-BCSI: A deep learning-based framework for bias correction and spatial imputation of PM2.5 concentrations in South Korea

Deep learning solver for solving advection–diffusion equation in comparison to finite difference methods

The Impact of Springtime‐Transported Air Pollutants on Local Air Quality With Satellite‐Constrained NOx Emission Adjustments Over East Asia

Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system

Hourly and Daily PM 2.5 Estimations Using MERRA‐2: A Machine Learning Approach

The Impact of Springtime‐Transported Air Pollutants on Local Air Quality With Satellite‐Constrained NO x Emission Adjustments Over East Asia

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