Juanhao Zhao
Datos Biográficos
| ID | 7900463 |
|---|---|
| NOMBRE | Juanhao Zhao |
| NOMBRES | Juanhao |
| APELLIDO | Zhao |
| FIRMA | ZHAO J |
| AFILIACIONES | University of Southern California |
| VERIFICADO | No |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2020 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2022 |
| ÍNDICE H | 0 |
Regional Prediction of Ozone and Fine Particulate Matter Using Diffusion Convolutional Recurrent Neural Network
Accurate air quality forecasts can provide data-driven supports for governmental departments to control air pollution and further protect the health of residents. However, existing air quality forecasting models mainly focus on site-specific time series forecasts at a local level, and rarely consider the spatiotemporal relationships among regional monitoring stations. As a novelty, we construct a diffusion convolutional recurrent neural network (…
Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model
The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident's outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In or…
Sin obras prominentes en esta página.
Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model
The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident's outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In or…
Regional Prediction of Ozone and Fine Particulate Matter Using Diffusion Convolutional Recurrent Neural Network
Accurate air quality forecasts can provide data-driven supports for governmental departments to control air pollution and further protect the health of residents. However, existing air quality forecasting models mainly focus on site-specific time series forecasts at a local level, and rarely consider the spatiotemporal relationships among regional monitoring stations. As a novelty, we construct a diffusion convolutional recurrent neural network (…
Air pollution (2 obras) · Air Quality and Health Impacts (2 obras) · Air quality index (2 obras) · Air Quality Monitoring and Forecasting (2 obras) · Computer Science (2 obras) · Environmental Science (2 obras) · Geography (2 obras) · Machine learning (2 obras) · Meteorology (2 obras) · Particulates (2 obras)