Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019403" target="_blank" >RIV/62690094:18470/22:50019403 - isvavai.cz</a>

Result on the web

<a href="https://www.techscience.com/iasc/v33n3/47103" target="_blank" >https://www.techscience.com/iasc/v33n3/47103</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/iasc.2022.023962" target="_blank" >10.32604/iasc.2022.023962</a>

Result language

angličtina

Original language name

Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

Original language description

In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources inves-tigati on, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The phy-sical coordination of these sensor nodes is determined, and it is called as localiza-tion. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT device in the network. With despite this, among the various applications, air pollution and air quality monitoring having many issues on how to place the sensor network in a wide area to monitor the air pollutants level such as carbon dioxide (CO2), nitrogen dioxides (NO2), particulate matter (PM), sulphur dioxide (SO2), ammonia (NH3) and other toxic gases involved in human and industrial activities. The responsibility of the WSN in air quality monitoring is to be posi-tioning the sensor nodes in the large area with low cost and also gather the real time data and produce the monitoring system as an accurate one. In this proposed work, deep learning-based approach called dual graph convolution and LSTM (Long Short-Term Memory) network based (air quality index) AQI predictions were performed. This uses the infrared based technology to measure the CO2, temperature and humidity, Geo statistic method and low power wireless network-ing. Accuracy of the proposed system is maximum of 95% which is higher than existing techniques.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Intelligent Automation &amp; Soft Computing: An International Journal

ISSN

1079-8587

e-ISSN

2326-005X

Volume of the periodical

33

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1639-1652

UT code for WoS article

000778567600005

EID of the result in the Scopus database

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