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 method for estimating air quality at unobserved locations during a specific period. Inductive spatio-temporal kriging with increment training strategy has demonstrated its effectiveness using virtual nodes to simulate unobserved nodes. However, a disparity between virtual and real nodes persists, complicating the application of learning patterns derived from virtual nodes to actual unobserved ones. To address these limitations, this paper presents a Physics-Guided Increment Training Strategy (PGITS). Specifically, we design a dynamic graph generation module to incorporate the advection and diffusion processes of airborne particles as physical knowledge into the graph structure, dynamically adjusting the adjacency matrix to reflect physical interactions between nodes. By using physics principles as a bridge between virtual and real nodes, this strategy ensures the features of virtual nodes and their pseudo labels are closer to actual nodes. Consequently, the learned patterns of virtual nodes can be applied to actual unobserved nodes for effective kriging.
Code (0)
등록된 구현이 없습니다.
Tasks
Air Quality InferenceGraph GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Uniform Inductive Spatio-Temporal Kriging
Inductive spatio-temporal kriging infers signals at unobserved locations from observed sensors, but real-world observations are often incomplete and exhibit block-wise missingness caused by failures, interruptions, or ma…
Physics-Guided Inductive Spatiotemporal Kriging for PM2.5 with Satellite Gradient Constraints
High-resolution mapping of fine particulate matter (PM2.5) is a cornerstone of sustainable urbanism but remains critically hindered by the spatial sparsity of ground monitoring networks. While traditional data-driven met…
Inductive Graph Neural Networks for Spatiotemporal Kriging
Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis. Recent research on graph neural networks has made substantial progress in time series forecasting, whil…
Graph Neural NetworkTime SeriesTime Series AnalysisTime Series ForecastingDRIK: Distribution-Robust Inductive Kriging without Information Leakage
Inductive kriging supports high-resolution spatio-temporal estimation with sparse sensor networks, but conventional training-evaluation setups often suffer from information leakage and poor out-of-distribution (OOD) gene…
Domain GeneralizationINCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aim…
Graph Representation LearningInductive LearningRelationRepresentation Learning