scholarly journals IoT enabled Environmental Air Pollution Monitoring and Rerouting system using Machine learning algorithms

2020 ◽  
Vol 955 ◽  
pp. 012005
Author(s):  
Leeban Moses ◽  
Tamilselvan ◽  
Raju ◽  
Karthikeyan
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Environmental Air ◽  
Environmental Air Pollution

Open, IoT powered Environmental Air Pollution Monitoring Framework for Traffic Management

2021 ◽  
Author(s):  
M. A. L. S. K. Manchanayaka ◽  
J. P. D. Wijesekara ◽  
Chan-Yun Yang ◽  
C. Premachandra ◽  
M. F. M. Firdhous ◽  
...  
Keyword(s):  
Air Pollution ◽  
Traffic Management ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Monitoring Framework ◽  
Environmental Air ◽  
Environmental Air Pollution

scholarly journals IoT BASED AIR AND SOUND POLLUTION MONITIORING SYSTEM USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
M.RAMANA REDDY
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Monitoring System ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Raspberry Pi

Air pollution is the largest environmental and public health challenge in the world today. Air pollution leads to adverse effects on human health, climate and ecosystem. Air is getting polluted because of release of Toxic gases by industries, vehicular emissions and increased concentration of harmful gases and particulate matter in the atmosphere. In order to overcome these issues an IoT based air and sound pollution monitoring system is designed. To design this monitoring system, machine learning algorithms K-NN and Naive Bayes are used. K-Nearest Neighbour and Naive Bayes are machine learning algorithms used to predict the status of pollution present in the environment. In this system, analog to digital converter, global service mobile communication, temperature sensor, humidity sensor, carbon monoxide and sound sensors are interfaced with raspberry pi using serial cable. The sensor data is uploaded in thinkspeak (IoT) and webpage. This data is compared with the trained data to check accuracy. To calculate the accuracy of both algorithms, Python code is developed using python software tool.

scholarly journals Intelligent Air Pollution Monitoring System for Smart Cities Using IoT and Machine Learning

2021 ◽  
Vol 10 (2) ◽  
pp. 1399-1407
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
Control Measures ◽  
Pollution Monitoring ◽  
World Health ◽  
Air Pollution Monitoring ◽  
Pollution Levels ◽  
The World

Today, almost everything is going under automation. Air pollution has become one of the major crises across the globe. According to the report of the World Health Organization (WHO), around 580,000 people died due to air pollution. This document deals with the effective monitoring of air pollution systems. The proposed technique uses machine learning algorithms for the intelligent monitoring of air pollution. The concept of the Internet of Things (IoT) is implemented in the system to make it more reliable and accessible from anywhere throughout the world. ESP32 is used as a microprocessor for decision making purposes. The system uses Arduino software to build an algorithm. The DHT11 module is used to sense the humidity as well as temperature. MQ-2, MQ-7 and MQ-135 are used for sensing the level of methane, carbon monoxide and for measuring air quality, respectively. A buzzer is used to identify any unusual condition. Our work considers pollution caused by vehicles and provides an in-the-moment solution that does not directly monitor pollution levels, as well as control measures for reducing traffic in extremely polluted areas. This system will undoubtedly be on humans' behalf in such a way that a smart city will have much less time for spending, and there will undoubtedly be other industries, and the air will undoubtedly be extra polluted, and this device will undoubtedlyallow people to understand how safe the air is.

scholarly journals Air Quality Index Prediction using Machine Learning Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 7489-7492
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Learning Algorithms ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Quality Data ◽  
Support Vector

— 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.

PM2.5 Concentration Prediction for Air Pollution using Machine Learning Algorithms

2019 ◽  
Vol 28 (1) ◽  
pp. 349-354 ◽  
Author(s):  
Ahmed Samy Abd El Aziz Moursi ◽  
Marwa Shouman ◽  
Ezz El-din Hemdan ◽  
Nawal El-Fishawy
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

scholarly journals Air Quality Prediction by Classification of Supervised Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 516-521
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
User Interface ◽  
Air Quality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Quality Prediction ◽  
Air Quality Prediction

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

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