In this paper, we propose a novel incomplete multimodal learning approach (iMMAir) to impute missing data for robust air quality prediction. Specifically, we first design a shallow feature extractor to capture modal-specific features within the embedded space. Then we develop a conditional diffusion...
This repo is the Pytorch implementation of our manuscript titled AirPhyNet: Physics-Guided Neural Networks for Air Quality Prediction. In this study, we present a novel physics guided differential equation network for precise air quality prediction over the next 72 hours with a physical meaning. Th...
Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pollution, potentially leading to physically inc
Air quality prediction is a hot topic in the environmental field, and the common prediction methods are three main categories: numerical simulation, statistical methods, and machine learning. Earlier studies on air quality prediction mostly used numerical simulation. Using mathematical knowledge, it build...
.github CCTM DOCS POST PREP PYTOOLS UTIL .gitignore README.md bldit_project.csh config_cmaq.csh license.md README MIT license CMAQv5.5 US EPA Community Multiscale Air Quality Model (CMAQ) Website:https://www.epa.gov/cmaq CMAQ is an active open-source development project of the U.S. ...
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The performance of the proposed solution is evaluated by simulation, and it is shown that there is a significant performance gain when the location deployment and energy optimization strategies are adopted, and the prediction algorithm can effectively predict the future air quality. × Upload graph ...
Positive SHAP values indicate a feature pushing the model prediction higher, and vice versa. The results reveal that the day-of-year and wind speed exert the largest negative and positive impacts on air quality, respectively (Fig. 2d). Download: Download high-res image (633KB) Download: ...
This github repository corresponds to our paper accepted by Information fusion (MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction). In order for MGSFformer to adapt to classic multivariate time series forecasting tasks, we slightly modified the model's input...
ADNNet: Attention-based deep neural network for Air Quality Index prediction Introduction This repository provides the implementation of ADNNet for AQI prediction. The experiments have been performed datasets:AQI Online Testing and Analysis Platform. ...