Forecasting Fine-Grained Air Quality Based on Big Data

By Yu Zheng, Xiuwen Yi, Ming Li, Ruiyuan Li, Zhang Shan, Eric Chang, Tianrui Li. 2015

519 citations recorded by the source corpus.

Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Yu Zheng
2. Jiawei Han

In Environmental Engineering

1. Yu Zheng
2. Junbo Zhang
3. Tianrui Li
4. Xiuwen Yi
5. Qiang Yang
6. Ruiyuan Li
7. Ying Wei
8. Philip S. Yu

In Health, Toxicology and Mutagenesis

1. Yu Zheng
2. Tianrui Li
3. Qiang Yang
4. Ying Wei

Citations in the corpus

• A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “4 IoT Data Fusion Requirements Data fusion can be efficiently applied to reconstruct and fill missing values or data [2, 46, 47], to conduct prediction [48, 49], to identify the causality of specific events and give a conclusion about causal connections [50, 51], and to detect anomalies [52], etc.”
• Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “A branch of research also partitions a city into grids, and then studies the traffic flow in each region of the city, such as predicting urban air quality [47,48], detecting anomalous traffic patterns [49], inferring missing air quality [50], forecasting of spatio-temporal data [3].”
• Deep Air Quality Forecasting Using Hybrid Deep Learning Framework (2018, methodology): “out a city through a semisupervised inference - model [8]. Zheng et al. also proposed a realtime air qual-- ity forecasting framework which uses data-driven models to predict fine-grained air quality [19]. More recently, deep learning has been widely applied to sequence data process and time series ”
• Frequency-domain MLPs are More Effective Learners in Time Series Forecasting (2023, background): “Time series forecasting has been a critical role in a variety of real-world industries, such as climate condition estimation [1, 2], traffic state prediction [3, 4], economic analysis [5, 6], etc.”
• Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2016, background): “• ST-ANN: It first extracts spatial (nearby 8 regions’ value) and temporal (8 previous time intervals) features, then fed into an artificial neural network (Zheng et al. 2015).”
• FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective (2023, unknown): “Multivariate time series (MTS) forecasting plays an important role in numerous real-world scenarios, such as traffic flow prediction in transportation systems [1, 2], temperature estimation in weather forecasting [3, 4], and electricity consumption planning in the energy market [5, 6], etc.”
• Transfer Knowledge between Cities (2016, background): “For example, to predict and tackle air pollution, we need to check air quality data from monitoring stations, pollution emission from factories and vehicles, land uses and meteorological data of di ff erent locations [36, 38].”
• p-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data (2016, background): “[12][13][14] propose data-driven approaches to infer and forecast fine-grained air quality using heterogeneous urban data.”
• Deep Distributed Fusion Network for Air Quality Prediction (2018, methodology): “FFA [7]: State-of-the-art air quality prediction model that is multi-view-based hybrid model considering spatial correlations, temporal dependencies, and sudden changes.”
• Methodologies for Cross-Domain Data Fusion: An Overview (2015, background): “Future [89], Causality Inference, Object Profiling [73], [74], [76], and Anomaly Detection [15], [49], [90], etc.”