SVR based dense air pollution estimation model using static and wireless sensor network

Hu, Ke; Sivaraman, Vijay; Bhrugubanda, Hari; Shi-ying, Kang; Rahman, Ashfaqur

doi:10.1109/icsens.2016.7808827

Cited by 13 publications

(10 citation statements)

References 11 publications

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“…EC sensors are the other popular low-cost technology for monitoring gas concentrations. EC sensors have been used to monitor CO, NO, NO 2 , O 3 and sulphur dioxide (SO 2 ) [43,61,98,100,114,120,121,154]. These sensors detect gases by oxidation-reduction reactions, employing electrodes separated by an electrolyte substance, such as mineral acid.…”

Section: Electrochemical Sensorsmentioning

confidence: 99%

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Concas

Mineraud

Lagerspetz

et al. 2021

ACM Trans. Sen. Netw.

130

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The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

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Section: Electrochemical Sensorsmentioning

confidence: 99%

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Concas

Mineraud

Lagerspetz

et al. 2021

ACM Trans. Sen. Netw.

130

View full text Add to dashboard Cite

show abstract

“…The proposed work is an attempt to accurately predict PM 2.5 level and to improve the accuracy of forecasts, especially in the atmosphere of Delhi. A model for this is proposed, based on Extra-Trees-Regressor [24] improved with Ada Boost [25]. Extra-Trees is a very casual tree set technique both the choice of the interception and the attributes involved separate tree nodes.…”

Section: Related Workmentioning

confidence: 99%

Review on air pollution of Delhi zone using machine learning algorithm

Sinha

Singh

2021

JAPH

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The issue of pollution in urban cities is a major problem these days especially in cities like the New Delhi is detected with more number of toxic gases in air, which has deduced the air quality of New Delhi. Thus, predictive analytics play a significant role in predicting the future instances of air quality based on the historical data. Forecasting the air quality of these cities is mandatory to overcome its consequences. Several machines learning algorithm is widely used these days to predict the future instances. Such as random forest, support vector machine, regression, classification, and so on. Main pollutants which present in the air are PM2.5, PM10, CO, NO2 , SO2 and O3 . In this paper we have focused mainly on data set of New Delhi for predicting ambient air pollution and quality using several machines learning algorithm.

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“…A supervised regression model is developed based on historical data of air pollution in Sydney [2] which surpassed its contemporary ANN's in terms of accuracy in prediction and has high spatial resolution.…”

Section: Literature Surveymentioning

confidence: 99%

Forecasting Hazard Level of Air Pollutants Using LSTM’s

Gul

Khan

2020

IFIP Advances in Information and Communication Technology

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The South Asian countries have the most polluted cities in the world which has caused quite a concern in the recent years due to the detrimental effect it had on economy and on health of humans and crops. PM 2.5 in particular has been linked to cardiovascular diseases, pulmonary diseases, increased risk of lung cancer and acute respiratory infections. Higher concentration of surface ozone has been observed to have negatively impacted agricultural yield of crops. Due to its deleterious impact on human health and agriculture, air pollution cannot be brushed off as a trivial matter and measures must be taken to address the problem. Deterministic models have been actively used; but they fall short due to their complexity and inability to accurately model the problem. Deep learning models have however shown potential when it comes to modeling time series data. This article explores the use of recurrent neural networks as a framework for predicting the hazard levels in Lahore, Pakistan with 95.0% accuracy and Beijing, China with 98.95% using the time series data of air pollutants and meteorological parameters. Forecasting air quality index (AQI) and Hazard levels would help the government take appropriate steps to enact policies to reduce the pollutants and keep the citizens informed about the statistics.

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SVR based dense air pollution estimation model using static and wireless sensor network

Cited by 13 publications

References 11 publications

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

Review on air pollution of Delhi zone using machine learning algorithm

Forecasting Hazard Level of Air Pollutants Using LSTM’s

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