Cover Image

Interpretable Air Pollution Forecasting by Physics-Guided Spatiotemporal Decoupling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Zhiguo, Ma, Xiaoliang, Schlesinger, Daniel
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Causality-Aware Spatiotemporal Model for Multi-Region and Multi-Pollutant Air Quality Forecasting
by: Lu, Junxin, et al.
Published: (2025)

EmissionNet: Air Quality Pollution Forecasting for Agriculture
by: Saligram, Prady, et al.
Published: (2025)

Air Pollution Forecasting in Bucharest
by: Şerban, Dragoş-Andrei, et al.
Published: (2025)

Incident-Guided Spatiotemporal Traffic Forecasting
by: Fan, Lixiang, et al.
Published: (2026)

Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
by: Hu, Junfeng, et al.
Published: (2021)

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series
by: Song, Xianhao, et al.
Published: (2026)

Spatiotemporal Causal Decoupling Model for Air Quality Forecasting
by: Ma, Jiaming, et al.
Published: (2025)

A review on development of eco-friendly filters in Nepal for use in cigarettes and masks and Air Pollution Analysis with Machine Learning and SHAP Interpretability
by: Paneru, Bishwash, et al.
Published: (2025)

Collaborative Deterministic-Probabilistic Forecasting for Diverse Spatiotemporal Systems
by: Sheng, Zhi, et al.
Published: (2025)

Physics-Guided Inductive Spatiotemporal Kriging for PM2.5 with Satellite Gradient Constraints
by: Wang, Shuo, et al.
Published: (2025)

PCDCNet: A Surrogate Model for Air Quality Forecasting with Physical-Chemical Dynamics and Constraints
by: Wang, Shuo, et al.
Published: (2025)

GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models through Statistically-Guided Geo-Prototyping
by: An, Bang, et al.
Published: (2024)

Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset
by: Shukla, Manish
Published: (2025)

BeamVQ: Beam Search with Vector Quantization to Mitigate Data Scarcity in Physical Spatiotemporal Forecasting
by: Wang, Weiyan, et al.
Published: (2025)

Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
by: Wang, Fu, et al.
Published: (2026)

Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models
by: Gondal, Moazzam Umer, et al.
Published: (2026)

STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
by: Wang, Hongjun, et al.
Published: (2024)

AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting
by: Wu, Binqing, et al.
Published: (2026)

S^2-KD: Semantic-Spectral Knowledge Distillation Spatiotemporal Forecasting
by: Wang, Wenshuo, et al.
Published: (2025)

Error Adjustment Based on Spatiotemporal Correlation Fusion for Traffic Forecasting
by: Liu, Fuqiang, et al.
Published: (2025)

Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
by: Marisca, Ivan, et al.
Published: (2024)

Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
by: Abubakkar, Md, et al.
Published: (2026)

Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network
by: Yin, Hang, et al.
Published: (2025)

Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting
by: Luo, Yingtao, et al.
Published: (2025)

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
by: Wu, Hao, et al.
Published: (2026)

Spatiotemporal-aware Trend-Seasonality Decomposition Network for Traffic Flow Forecasting
by: Cao, Lingxiao, et al.
Published: (2025)

AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting
by: Lyu, Tengfei, et al.
Published: (2024)

Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
by: Gryech, Ihsane, et al.
Published: (2024)

Towards Interpretable and Inference-Optimal COT Reasoning with Sparse Autoencoder-Guided Generation
by: Zhao, Daniel, et al.
Published: (2025)

Graph Similarity Computation via Interpretable Neural Node Alignment
by: Wang, Jingjing, et al.
Published: (2024)

AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
by: Hettige, Kethmi Hirushini, et al.
Published: (2024)

Deep Learning for Crime Forecasting: The Role of Mobility at Fine-grained Spatiotemporal Scales
by: Zumel, Ariadna Albors, et al.
Published: (2025)

Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting
by: Wang, Zhongying, et al.
Published: (2025)

DecoKAN: Interpretable Decomposition for Forecasting Cryptocurrency Market Dynamics
by: Gao, Yuan, et al.
Published: (2025)

Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Models
by: Wu, Hao, et al.
Published: (2025)

Towards Generalizable PDE Dynamics Forecasting via Physics-Guided Invariant Learning
by: Li, Siyang, et al.
Published: (2025)

Deciphering Invariant Feature Decoupling in Source-free Time Series Forecasting with Proxy Denoising
by: Yan, Kangjia, et al.
Published: (2025)

Interpretable Load Forecasting via Representation Learning of Geo-distributed Meteorological Factors
by: Zhou, Yangze, et al.
Published: (2025)

From $\log π$ to $π$: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight
by: Fu, Xiaoliang, et al.
Published: (2026)

A Multi-View Multi-Timescale Hypergraph-Empowered Spatiotemporal Framework for EV Charging Forecasting
by: Li, Jinhao, et al.
Published: (2025)