PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

• URL: http://arxiv.org/abs/2002.12898v2
• Date: Mon, 11 Oct 2021 10:20:31 GMT
• Title: PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
• Authors: Shuo Wang, Yanran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao
• Abstract summary: We develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. The proposed model has been deployed online to provide free forecasting service.
• Score: 15.587337304295819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.

Related papers

• Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet) [0.3848364262836075]
  We propose a dual deep neural network (D-DNet) prediction and data assimilation system that efficiently integrates real-time observations. D-DNet excels in global operational forecasting for PM2.5 and AOD550, maintaining consistent accuracy throughout the entire year of 2019. It demonstrates notably higher efficiency than the Copernicus Atmosphere Monitoring Service (CAMS) 4D-Var operational forecasting system.
  arXiv  Detail & Related papers  (2024-06-27T13:14:20Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Uncertainty Quantification over Graph with Conformalized Graph Neural Networks [52.20904874696597]
  Graph Neural Networks (GNNs) are powerful machine learning prediction models on graph-structured data. GNNs lack rigorous uncertainty estimates, limiting their reliable deployment in settings where the cost of errors is significant. We propose conformalized GNN (CF-GNN), extending conformal prediction (CP) to graph-based models for guaranteed uncertainty estimates.
  arXiv  Detail & Related papers  (2023-05-23T21:38:23Z)
• Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction [59.221668173521884]
  We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
  arXiv  Detail & Related papers  (2022-04-18T09:25:06Z)
• 2.5D Visual Relationship Detection [142.69699509655428]
  We study 2.5D visual relationship detection (2.5VRD) Unlike general VRD, 2.5VRD is egocentric, using the camera's viewpoint as a common reference for all 2.5D relationships. We create a new dataset consisting of 220k human-annotated 2.5D relationships among 512K objects from 11K images.
  arXiv  Detail & Related papers  (2021-04-26T17:19:10Z)
• A Novel Prediction Approach for Exploring PM2.5 Spatiotemporal Propagation Based on Convolutional Recursive Neural Networks [7.131106953836335]
  The prediction system of PM2.5 propagation provides more detailed and accurate information as an early warning system to reduce health impacts on the community. This research was conducted by using dataset of air quality monitoring systems in Taiwan. In general, the model is able to provide accurate predictive results by considering the bonds among measurement nodes in both spatially and temporally.
  arXiv  Detail & Related papers  (2021-01-15T17:00:04Z)
• A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network [2.2175470459999636]
  This study develops a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise. The forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the time series model, artificial neural network, and the popular deep learning models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations.
  arXiv  Detail & Related papers  (2020-12-07T15:22:01Z)
• Dense Forecasting of Wildfire Smoke Particulate Matter Using Sparsity Invariant Convolutional Neural Networks [2.2835610890984164]
  Forecasts of fine particulate matter (PM 2.5) from wildfire smoke are crucial to safeguarding public cardiopulmonary health. Existing forecasting systems are trained on sparse and inaccurate ground truths, and do not take sufficient advantage of important spatial inductive biases. We present a convolutional neural network which preserves sparsity invariance throughout, and leverages multitask learning to perform dense forecasts of PM 2.5values.
  arXiv  Detail & Related papers  (2020-09-23T20:13:35Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)
• Analytical Equations based Prediction Approach for PM2.5 using Artificial Neural Network [0.0]
  Particulate Matter (PM2.5) is one of the important particulate pollutants to measure the Air Quality Index (AQI) The conventional instruments used by the air quality monitoring stations to monitor PM2.5 are costly, bulkier, time-consuming, and power-hungry. This article presents analytical equations based prediction approach for PM2.5 using an Artificial Neural Network (ANN)
  arXiv  Detail & Related papers  (2020-02-26T11:39:18Z)
• Assessing Graph-based Deep Learning Models for Predicting Flash Point [52.931492216239995]
  Graph-based deep learning (GBDL) models were implemented in predicting flash point for the first time. Average R2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3% lower and 2.0 K higher than previous comparable studies.
  arXiv  Detail & Related papers  (2020-02-26T06:10:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.