Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations

Soh, Ping-Wei; Chang, Jia‐Wei; Huang, Jen-Wei

doi:10.1109/access.2018.2849820

Cited by 202 publications

(98 citation statements)

References 25 publications

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“…MCS [17], [18] is a control structure built on the plant (i.e. the operation process of the system) which can be expressed as the realization of several partial achievements [19].…”

Section: Background Of Methodologymentioning

confidence: 99%

An Adaptive Indoor Air Quality Control Scheme for Minimizing Volatile Organic Compounds Density

Hung

Tsang

et al. 2020

IEEE Access

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Volatile organic compounds (VOCs) such as toluene, xylene, and formaldehyde are commonly found in indoor and the VOCs will yield human health's issue. The compounds are crucial in determining the indoor air quality (IAQ) and hence being how to manage IAQ becomes an important topic. Most human may spend most of time living in poor IAQ environment and it may result in excess life risk to respiratory symptoms and billion US dollars cost annually. VOC degrades IAQ and high VOC density indoor is not uncommon. The World Health Organization (WHO) and the Government of Canada provided benchmarks on the harm levels and the benchmarks indicated the potential health risk caused by hazardous substances. In this paper, a new comprehensive control scheme, namely fuzzy genetic multi-layer control scheme (FGMLCS), is designed to manage the IAQ. The multilayer control structure is designed which includes fuzzy logic together with genetic algorithm and multi-objective optimization to give an optimal control for a better IAQ. Q factor is defined based on the ''harm levels'' set by the benchmarks to give a unified standard for various VOCs with different ''harm levels''. FGMLCS has achieved VOC density better than the ''harm levels'' by over 57%, which is superior to the benchmarks and is able to lower the risk of health deterioration and thus aiding habitant to be less carcinogenic.

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“…MCS [17], [18] is a control structure built on the plant (i.e. the operation process of the system) which can be expressed as the realization of several partial achievements [19].…”

Section: Background Of Methodologymentioning

confidence: 99%

An Adaptive Indoor Air Quality Control Scheme for Minimizing Volatile Organic Compounds Density

Hung

Tsang

et al. 2020

IEEE Access

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“…In this section, we introduce the application of our proposed method that we used for monitoring air quality in Taiwan. There are several recent studies which have focused on air quality and PM2.5 forcasting [ 29 , 30 , 31 , 32 ], and anomaly detection in air quality [ 33 ].…”

Section: Pm25 Sensors Case Studymentioning

confidence: 99%

TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams

Huang

Zhong

Jaysawal

2020

Sensors

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Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.

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“…In this the author has taken from two sources of Taiwan and Beijing environmental monitoring system. Data collected from 76 locations of Taiwan from January 2014 to September 2017 and in Beging from May 2014 to April 2015 were used for prediction [3].…”

Section: Literature Surveymentioning

confidence: 99%

“…The air quality monitoring stations collects various pollutant concentrations and based on this AQI is calculated and the area is classified as good, satisfactory, moderately polluted, poor, very poor, or severe. Many researchers applied machine learning techniques to predict the AQI of future based on the past data [3].…”

Section: Introductionmentioning

confidence: 99%

Air Quality Index Prediction using Machine Learning Algorithms

Shanthi*¹,

Pyingkodi²

2019

IJRTE

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— The global environment is presently facing a key issue of air pollution. The four air pollutants which are becoming a concerning intimidation to human health are respirble particulate matter, nitrogen oxide, particle matter, and sulfur dioxide. A vast amount of air quality data is collected in different monitoring stations throughout the world. The collected data can be analyzed to forecast the air quality index (AQI) of future. This paper proposes machine learning algorithms such as random forest, support vector machine, self adaptive resource allocation to predict the future AQI. Tamil Nadu Pollution Control Board (TNPCN) deployed air pollution monitoring station in five regions. Air pollutant of PM10, PM2.5, SO2 and NO2 are monitord and AQI is calculated.. The data collected from January 2019 to November 2019 by TNPCN and also AQI of previous five years were used This system attempts to predict the level of pollutant PM,SO2,NO2 in the air to detect the AQI.

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Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations

Cited by 202 publications

References 25 publications

An Adaptive Indoor Air Quality Control Scheme for Minimizing Volatile Organic Compounds Density

An Adaptive Indoor Air Quality Control Scheme for Minimizing Volatile Organic Compounds Density

TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams

Air Quality Index Prediction using Machine Learning Algorithms

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