Air pollution control model using machine learning and IoT techniques

Shetty, Chetan; Sowmya, B.; Seema, S.; Srinivasa, K. G.

doi:10.1016/bs.adcom.2019.10.006

Cited by 21 publications

(9 citation statements)

References 2 publications

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“…The recent research contributions were the main focus of the study though a few important research studies, conducted and investigated in last two decades, were also included. The contributions were reported on various SEM methods used for several purposes, mainly air quality assessment [1,5,11,12,47,58,76,85,89,90]; water pollution monitoring methods [1,13,14,39,64,66,[71][72][73][91][92][93][94][95][96][97]; radiation monitoring methods [1,36]; and smart agriculture monitoring systems [1,14,28,54,60,62,63,[98][99][100][101][102].…”

Section: Discussion Analysis and Recommendationmentioning

confidence: 99%

“…Data captured through smart sensor nodes were processed and analyzed with the help of machine learning techniques. Another air quality control process was studied using IoT and machine learning techniques in [76], with a focus on assessment of air pollution, deploying gas sensors which help in capturing air particles and analyzing the pollutants mixed in the air. Sensor networks have been established in moving vehicles for monitoring air quality with the help of machine learning; in [77], mobile sensor nodes and WSN were deployed.…”

Section: Study Based On Smart Water Pollution Monitoring (Swpm) Systemsmentioning

confidence: 99%

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Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

323

109

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Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

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Section: Discussion Analysis and Recommendationmentioning

confidence: 99%

Section: Study Based On Smart Water Pollution Monitoring (Swpm) Systemsmentioning

confidence: 99%

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Ullo

Sinha

2020

Sensors

323

109

View full text Add to dashboard Cite

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“…(1) Chromatography Chromatographic detection of chemicals such as dioxins requires initial extraction and purification of the analytes in the sample, followed by separation using chromatographic columns and quantitative and qualitative analysis using similar detectors [11].…”

Section: Commonly Used Detection Methods For Dioxin Compoundsmentioning

confidence: 99%

Environmental Biotechnology in Environmental Pollution Control Based on Data Mining Algorithm

2022

AJEB

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With the continuous development of computer technology, the application of computer technology, especially big data analysis technology, will be able to process a large amount of environmental monitoring information system data. Because the data sources of environmental monitoring information system data are complex and the amount of data is huge, this is also able to Mining the innate advantages in processing such characteristics of environmental monitoring information data. The purpose of this paper is to study the application of environmental biotechnology in environmental pollution control based on data mining algorithm. Using data mining technology, based on the evaluation of dioxin activity, the M lake was sampled, the dioxin activity of organic extracts in water and biological samples was detected, the main active components were identified, and the dioxin causing toxicity was identified. The experimental results show that all organic extracts of biological samples can cause aromatic hydrocarbon receptor activity, and dioxin-like substances accumulate seriously in shellfish. Provide certain theoretical basis and technical support for the further management and governance of M Lake.

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“…The sensor is applied in the detection of pollutants, discharged by vehicles, such as carbon monoxide (CO), Ozone, Sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and Particulate Matter (PM2.5). In literature, various kinds of sensors such as MQ135, MQ7, and MQ2 are employed to gather distinct kinds of emission information [15]. MQ7 is highly sensitive to carbon monoxide.…”

Section: The Proposed Modelmentioning

confidence: 99%

Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

Dutta¹,

Sampson²,

Ahmad³

et al. 2022

Computers, Materials &Amp; Continua

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Intelligent Transportation Systems (ITS) have become a vital part in improving human lives and modern economy. It aims at enhancing road safety and environmental quality. There is a tremendous increase observed in the number of vehicles in recent years, owing to increasing population. Each vehicle has its own individual emission rate; however, the issue arises when the emission rate crosses a standard value. Owing to the technological advances made in Artificial Intelligence (AI) techniques, it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution. The current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring (OSSO-HDLAPM) in ITS environment. The proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of pollutants. In addition, hybridized Convolution Neural Network with Long Short-Term Memory (HCNN-LSTM) model is used to predict pollutant level based on the data attained earlier by the sensors. In HCNN-LSTM model, the hyperparameters are selected and optimized using OSSO algorithm. In order to validate the performance of the proposed OSSO-HDLAPM technique, a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

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Air pollution control model using machine learning and IoT techniques

Cited by 21 publications

References 2 publications

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Environmental Biotechnology in Environmental Pollution Control Based on Data Mining Algorithm

Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

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