An IoT-based System to Evaluate Indoor Air Pollutants Using Grey Relational Analysis

Rastogi, Krati; Lohani, Divya; Acharya, Debopam

doi:10.1109/comsnets48256.2020.9027308

Cited by 5 publications

(8 citation statements)

References 12 publications

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“…However, the ADFIST model converged at different levels due to the fast convergence performance of PSO with a unique set of input–output combinations and rules. The ADFIST model performance is measured in terms of four essential performance indicators: NRMSE, NMSE, R 2 , and MAPE [ 70 , 71 , 72 ]. The main reason behind the selection of NRMSE and NMSE for model evaluation is that field data includes multiple parameters with different units and varying ranges [ 73 , 74 , 75 ].…”

Section: Resultsmentioning

confidence: 99%

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

Saini

Dutta

Marques

2022

Sensors

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Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.

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Section: Resultsmentioning

confidence: 99%

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

Saini

Dutta

Marques

2022

Sensors

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“…The magnitude of relative error (MRE) and mean absolute percentage error (MAPE) are mostly used to measure the accuracy of regression based prediction model [20,21]. The performance of the proposed scheme is evaluated using the magnitude of relative error (MRE) and mean absolute percentage error (MAPE).…”

Section: Evaluation Methodsmentioning

confidence: 99%

A novel framework for COVID-19 case prediction through piecewise regression in India

Senapati

Nag

Mondal

et al. 2020

Int. j. inf. tecnol.

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“…However, the simulation models assume and ignore some parameters, such as air change rate and occupancy density, leading to the need for further validation of the effectiveness of such models [118]. The duration of monitoring varies from 7.3 hours [118] to over 1 year [112], which means the data include information on diurnal variations and seasonal changes. Since the routine in school buildings is fixed, the monitoring time or simulation time was chosen for working hours [112,115,117,118].…”

Section: Data Collectionmentioning

confidence: 99%

“…The duration of monitoring varies from 7.3 hours [118] to over 1 year [112], which means the data include information on diurnal variations and seasonal changes. Since the routine in school buildings is fixed, the monitoring time or simulation time was chosen for working hours [112,115,117,118].…”

Section: Data Collectionmentioning

confidence: 99%

“…Factor selection in the nine papers reviewed can be divided into: select indicators influencing IAQ, i.e., output variables, and select input variables for predictive models. In Table 2, Rastogi et al [112] used Grey Relational Analysis for screening pollutants that have the most significant impact on IAQ. They measured six indoor parameters (temperature, humidity, CO 2 , CO, PM 2.5 , PM 10 ) and screened PM 2.5 and PM 10 as the output of the predictive model.…”

Section: Factor Selectionmentioning

confidence: 99%

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A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

Dong

Goodman

Rajagopalan

2023

IJERPH

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Background: Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application of data-driven models, such as artificial neural networks (ANNs) that can be used to predict levels of pollutants and indoor exposures. Methods: This review summarises the types and sources of indoor air pollutants (IAP) and the indicators of IAQ. This is followed by a systematic evaluation of ANNs as predictive models of IAQ in schools, including predictive neural network algorithms and modelling processes. The methods for article selection and inclusion followed a systematic, four-step process: identification, screening, eligibility, and inclusion. Results: After screening and selection, nine predictive papers were included in this review. Traditional ANNs were used most frequently, while recurrent neural networks (RNNs) models analysed time-series issues such as IAQ better. Meanwhile, current prediction research mainly focused on using indoor PM2.5 and CO2 concentrations as output variables in schools and did not cover common air pollutants. Although studies have highlighted the impact of school building parameters and occupancy parameters on IAQ, it is difficult to incorporate them in predictive models. Conclusions: This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.

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An IoT-based System to Evaluate Indoor Air Pollutants Using Grey Relational Analysis

Cited by 5 publications

References 12 publications

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

A novel framework for COVID-19 case prediction through piecewise regression in India

A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

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