A Crowdsource-Based Sensing System for Monitoring Fine-Grained Air Quality in Urban Environments

Huang, Jingchang; Duan, Ning; Ji, Peng; Ma, Chunyang; Hu, Feng; Ding, Yang; Yu, Yipeng; Zhou, Qianwei; Sun, Wei

doi:10.1109/jiot.2018.2881240

Cited by 67 publications

(42 citation statements)

References 16 publications

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“…IN recent years, people are beginning to pay more and more attention to the impact of the environment on health, and the information related to air quality has become the focus of people's daily life. The existing air quality monitoring instruments, stations and satellite meteorological data can provide real-time air quality monitoring information [1]. However, this is far from sufficient, and it is entirely necessary to predict the trend of air pollutants in the future.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

et al. 2019

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In recent years, people have been paying more and more attention to air quality because it directly affects people's health and daily life. Effective air quality prediction has become one of the hot research issues. However, this paper is suffering many challenges, such as the instability of data sources and the variation of pollutant concentration along time series. Aiming at this problem, we propose an improved air quality prediction method based on the LightGBM model to predict the PM2.5 concentration at the 35 air quality monitoring stations in Beijing over the next 24 h. In this paper, we resolve the issue of processing the high-dimensional large-scale data by employing the LightGBM model and innovatively take the forecasting data as one of the data sources for predicting the air quality. With exploring the forecasting data feature, we could improve the prediction accuracy with making full use of the available spatial data. Given the lack of data, we employ the sliding window mechanism to deeply mine the high-dimensional temporal features for increasing the training dimensions to millions. We compare the predicted data with the actual data collected at the 35 air quality monitoring stations in Beijing. The experimental results show that the proposed method is superior to other schemes and prove the advantage of integrating the forecasting data and building up the high-dimensional statistical analysis. INDEX TERMS Predictive data fusion, high dimensional statistical features, air quality prediction, machine learning.

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Section: Introductionmentioning

confidence: 99%

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

et al. 2019

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“…(3), respectively. min A (1) ,A (2) ,A (3) ω * (X − A (1) , A (2) , A (3) (1) ,A (2) ,A (3) ω * (X − G; A (1) , A (2) , A (3)…”

Section: B Tensor Completion Based Missing Data Imputationunclassified

“…In the above three equations, A (n) ∈ R I n ×R n is the n-th mode factor matrix, R n is the n-mode rank, n ∈ [1,3] is an integer, • F represents the Frobenius norm, and * denotes the elementwise product, named Hadamard product.…”

Section: B Tensor Completion Based Missing Data Imputationmentioning

confidence: 99%

“…In Eq. 3, G ∈ R R 1 ×R 2 ×R 3 is the core tensor, × n is the mode-n product of a tensor with a matrix; given a tensor Y ∈ (2) is the mode product of G ×1 A (1) with A (2) generating a tensor of size I 1 × I 2 × R 3 , and…”

Section: B Tensor Completion Based Missing Data Imputationmentioning

confidence: 99%

“…In past decades, technologies of data collection, storage and information processing have been greatly enhanced [1], [2]. This boosts the progress of connected vehicles and datadriven intelligent transportation systems (ITSs), in which massive accurate traffic data information are required to support the theory and application researches.…”

Section: Introduction a Motivationmentioning

confidence: 99%

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Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods

Tan

et al. 2020

IEEE Access

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Missing data is inevitable and ubiquitous in intelligent transportation systems (ITSs). A handful of completion methods have been proposed, among which the tensor-based models have been shown to be the most advantageous for missing traffic data imputation. Despite their superior imputation accuracies, the adoption of these imputed data is not uniform in modern ITSs applications. The primary goal of this paper is to explore how to use tensor completion methods to support ITSs. In particular, we study how to improve traffic flow prediction accuracy under different missing scenarios. Specifically, three common missing scenarios including element-wise random missing, time-structured missing, and space-structured missing are considered. Four classical tensor completion models including Smooth PARAFAC Decomposition based Completion (SPC), CP Decomposition-based (CP-WOPT) Completion, Tucker Decomposition-based Completion (TDI), and High-accuracy Low-rank Tensor Completion (HaLRTC) are used to impute the missing data. Four well-known prediction methods including Support Vector Regression (SVR), K-nearest Neighbor (KNN), Gradient Boost Regression Tree (GBRT), and Long Short-term Memory (LSTM) are tested. The simple mean value interpolation completed traffic data is regarded as the baseline data. The extensive experiments show that improvements of traffic flow prediction can be achieved by increasing missing traffic data imputation accuracy at most cases. Interestingly we find that prediction accuracy cannot be improved by an imputation model when the sparsely observed training datasets already provide sufficient training samples. INDEX TERMS Missing data imputation, missing traffic data, tensor completion, traffic flow prediction.

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Real‐Time and Image‐Based AQI Estimation Based on Deep Learning

Zhang

Tian

et al. 2022

Advcd Theory and Sims

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The real-time information on surrounding air quality index (AQI) is important for the public to protect themselves from air pollution. Traditional methods have some shortages regarding the estimation time and running efficiency. Consequently, the AQI results cannot meet the needs of personal protection and environmental management. With the popularity of smart terminals, it is easier to collect particular environmental images for AQI estimation tasks. Therefore, a real-time and image-based deep learning model named YOLO-AQI is proposed. Based on the object detection algorithms, the model has better performance regarding the AQI estimation speed. By optimizing the parameter transfer and network structure, the model takes an average of 0.0582 s to perform feature analysis and achieves 75.15% accuracy on AQI estimation tasks. Comparing YOLO-AQI with several image recognition models (VGG, AlexNet, GoogLeNet, MobileNet, and ResNet), it shows that YOLO-AQI outperforms other models by 14.8% on accuracy and by 71.1% on running speed. This method can provide real-time AQI level information for remote areas such as rural.

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A Crowdsource-Based Sensing System for Monitoring Fine-Grained Air Quality in Urban Environments

Cited by 67 publications

References 16 publications

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

A Predictive Data Feature Exploration-Based Air Quality Prediction Approach

Traffic Flow Prediction With Missing Data Imputed by Tensor Completion Methods

Real‐Time and Image‐Based AQI Estimation Based on Deep Learning

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