Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms
This paper proposes a deep learning model that integrates a convolutional neural network
and a gated recurrent unit with groups of neighboring stations to accurately predict PM2. 5 …
and a gated recurrent unit with groups of neighboring stations to accurately predict PM2. 5 …
Pollen forecasting and its relevance in pollen allergen avoidance
Pollinosis and allergic asthma are respiratory diseases of global relevance, heavily affecting
the quality of life of allergic subjects. Since there is not a decisive cure yet, pollen allergic …
the quality of life of allergic subjects. Since there is not a decisive cure yet, pollen allergic …
Deep Learning Estimation of Daily Ground‐Level NO2 Concentrations From Remote Sensing Data
The limited number of nitrogen dioxide (NO2) surface measurements calls for the
development of highly accurate approaches to estimating surface NO2 concentrations. In …
development of highly accurate approaches to estimating surface NO2 concentrations. In …
Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks
With rising levels of air-pollution, air-quality forecasting has become integral to the
dissemination of human health advisories and the preparation of mitigation strategies. To …
dissemination of human health advisories and the preparation of mitigation strategies. To …
A comprehensive study of the COVID-19 impact on PM2. 5 levels over the contiguous United States: A deep learning approach
We investigate the impact of the COVID-19 outbreak on PM 2.5 levels in eleven urban
environments across the United States: Washington DC, New York, Boston, Chicago, Los …
environments across the United States: Washington DC, New York, Boston, Chicago, Los …
Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter
From hourly ozone observations obtained from three regions⸻ Houston, Dallas, and West
Texas⸻ we investigated the contributions of meteorology to changes in surface daily …
Texas⸻ we investigated the contributions of meteorology to changes in surface daily …
A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance
Issues regarding air quality and related health concerns have prompted this study, which
develops an accurate and computationally fast, efficient hybrid modeling system that …
develops an accurate and computationally fast, efficient hybrid modeling system that …
Application of a partial convolutional neural network for estimating geostationary aerosol optical depth data
Satellite‐derived aerosol optical depth (AOD) is negatively impacted by cloud cover and
surface reflectivity. As these issues lead to biases, they need to be discarded, which …
surface reflectivity. As these issues lead to biases, they need to be discarded, which …
Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning
M Wei, X You - Water Resources Management, 2022 - Springer
Rainfall forecast is critical to the management and allocation of water resources. Deep
learning is used to predict rainfall time series with high temporal and spatial variability …
learning is used to predict rainfall time series with high temporal and spatial variability …
A deep convolutional neural network model for improving WRF simulations
Advancements in numerical weather prediction (NWP) models have accelerated, fostering a
more comprehensive understanding of physical phenomena pertaining to the dynamics of …
more comprehensive understanding of physical phenomena pertaining to the dynamics of …