U-Air: when urban air quality inference meets big data

By Yu Zheng, Furui Liu, Hsun-Ping Hsieh. 2013

974 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, 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 Environmental Engineering

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

In Global and Planetary Change

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

In Health, Toxicology and Mutagenesis

1. Yu Zheng
2. Tianrui Li
3. Qiang Yang
4. Ying Wei
5. Fang Deng
6. Jiachen Zhao
7. Jie Chen
8. Yeyun Cai

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.”
• A survey of data fusion in smart city applications (2019, methodology): “Mobile sensing works such as [82], [168] utilized different data sources to complete spatial resolution and visualized the ambient changes across the city.”
• Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. (2019, methodology): “The datasets used in this paper are collected by the Urban Air project of Microsoft Research (Zheng et al. (2013, 2015)).”
• p-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data (2016, background): “[28][29] propose data-driven approaches to infer and forecast fine-grained air quality us-ing heterogeneous urban data.”
• Forecasting Fine-Grained Air Quality Based on Big Data (2015, methodology): “As these parameters are difficult to obtain precisely, the results generated by such kinds of models may not be very accurate [14].”
• Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. (2017, background): “Therefore, obtaining air pollutant concentration information in real time is significant for air pollution control and the prevention of health issues due to air pollution (Zheng et al., 2013).”
• Mobile Crowd Sensing and Computing (2015, background): “U-Air [Zheng et al. 2013] inferred fine-grained air quality information (e.g., PM2.”
• Methodologies for Cross-Domain Data Fusion: An Overview (2015, background): “First, goals of fusing multiple datasets includes FillingMissing Values (of a sparse dataset) [53], [85], [87] [88], Predict”
• Deep Air Quality Forecasting Using Hybrid Deep Learning Framework (2018, methodology): “5, high humidity usually aggravates air pollution, and high atmospheric pressure usually results in good air quality [7], [19].”
• Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing (2017, methodology): “In [15], the prediction model is based on a few public air quality stations and meteorological data, taxi trajectories, road networks, and point of interests.”
• 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 learning architecture for air quality predictions (2016, background): “Obtaining real-time air quality information is of great importance for air pollution control and for protecting humans from adverse health impacts due to air pollution (Zheng et al. 2013).”
• Deep Distributed Fusion Network for Air Quality Prediction (2018, background): “For monitoring real-time air pollution, Chinese governments have built many air quality monitoring stations and published air quality data every hour in recent years [4].”
• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “Simulations are conducted based on two real-life datasets collected from sensor networks, i.e., the Sensor-Scope dataset [175] in the EPFL campus and the U-Air dataset of air quality readings in Beijing [176].”
• State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “R ECENTLY, the rapid development of the current Internet and mobile communications industry has contributed to increasingly large-scale, heterogeneous, dynamic, and systematically complex networks [1]–[3].”
• Trajectory Data Mining (2015, background): “To and leave an event frequently, the gathering pattern [Zheng et al. 2013a, 2014a] further loses the constraints of the aforementioned patterns by allowing the membership of a group to evolve gradually.”