Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

Zaidan, Martha Arbayani; Dada, Lubna; Alghamdi, Mansour A.; Al-Jeelani, H.; Lihavainen, Heikki; Hyvärinen, Antti; Hussein, Tareq

doi:10.3390/app9204475

Cited by 26 publications

(29 citation statements)

References 64 publications

(79 reference statements)

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“…A proxy can be defined as a mathematical model that estimates an air pollutant using other available measured variables [12]. The deployment of air pollutant proxy is beneficial to forecast air pollution level [13], to fill missing data from air quality database, and to substitute instruments that are typically expensive and complex in operations [14]. Proxies have been developed to estimate different air pollutant variables such as PM 10 [15], [16], PM 2.5 [15]- [17], CO [16], [18], NO 2 [16], [19], SO 2 [16], [20] and O 3 [14], [16], [21].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

et al. 2020

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This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.

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Section: Introductionmentioning

confidence: 99%

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

et al. 2020

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“…Hence, air pollutant proxies which enable measuring variables to be estimated virtually have become an alternative solution. Then, the proxies can be embedded into low-cost sensors [25]- [28].…”

Section: Discussionmentioning

confidence: 99%

Low-cost Air Quality Sensing Process: Validation by Indoor-Outdoor Measurements

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et al. 2020

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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Air pollution is a main challenge in societies with particulate matter PM2.5 as the major air pollutant causing serious health implications. Due to health and economic impacts of air pollution, low-cost and portable air quality sensors can be vastly deployed to gain personal air pollutant exposure. In this paper, we present an air quality sensing process needed for lowcost sensors which are planned for long-term use. The steps of this process include design and production, laboratory tests, field tests, deployment, and maintenance. As a case study we focus on the field test, where we use two generations of a portable air quality sensor (capable of measuring meteorological variables and PM2.5) to perform an indoor-outdoor measurement. The study found that all of the measurements shown to be consistent through validation among themselves. The sensors accuracy also demonstrate to be adequate by showing similar readings compared to the nearest air quality reference station.

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“…ANN has high abilities in self-learning, flexibility and non-linearity modeling. The method has become a well-known tool for classification, clustering, pattern recognition, and prediction in various scientific fields [44], air quality [45,46], environmental sciences [47,48], business intelligence [49], engineering applications [50] and applied physics [51].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

et al. 2020

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Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption.

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Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

Cited by 26 publications

References 64 publications

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

Low-cost Air Quality Sensing Process: Validation by Indoor-Outdoor Measurements

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

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