Papers Air Quality Inference
“Air Quality Inference” 태그가 달린 논문 10편 · 필터 해제
Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inference
The deployment of sensors for air quality monitoring is constrained by high costs, leading to inadequate network coverage and data deficits in some areas. Utilizing existing observations, spatio-temporal kriging is a met…
Air Quality InferenceGraph GenerationFine-gained air quality inference based on low-quality sensing data using self-supervised learning
Fine-grained air quality (AQ) mapping is made possible by the proliferation of cheap AQ micro-stations (MSs). However, their measurements are often inaccurate and sensitive to local disturbances, in contrast to standardi…
Air Quality InferenceSelf-Supervised LearningIncreasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
Multi-sensor ML models for EO aim to enhance prediction accuracy by integrating data from various sources. However, the presence of missing data poses a significant challenge, particularly in non-persistent sensors that …
Air Quality InferenceCrop ClassificationEarth ObservationMULTI-VIEW LEARNINGSpatio-Temporal Field Neural Networks for Air Quality Inference
The air quality inference problem aims to utilize historical data from a limited number of observation sites to infer the air quality index at an unknown location. Considering the sparsity of data due to the high mainten…
Air Quality InferenceMachine Learning for Urban Air Quality Analytics: A Survey
The increasing air pollution poses an urgent global concern with far-reaching consequences, such as premature mortality and reduced crop yield, which significantly impact various aspects of our daily lives. Accurate and …
Air Quality InferenceSurveyDeep Gaussian Processes for Air Quality Inference
Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pol…
Air Quality InferenceGaussian ProcessesVariational InferenceAIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities
Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to continuously obtain air quality information, but they do not cover all areas. …
Air Quality InferenceMixture-of-ExpertsAirRL: A Reinforcement Learning Approach to Urban Air Quality Inference
Urban air pollution has become a major environmental problem that threatens public health. It has become increasingly important to infer fine-grained urban air quality based on existing monitoring stations. One of the ch…
Air Quality Inferencereinforcement-learningReinforcement LearningReinforcement Learning (RL)Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monit…
Air Quality InferenceMatrix CompletionA Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
Urban air pollution has attracted much attention these years for its adverse impacts on human health. While monitoring stations have been established to collect pollutant statistics, the number of stations is very limite…
Air Quality Inference