An IoT-based System to Evaluate Indoor Air Pollutants Using Grey Relational Analysis
01 Jan 2020-pp 762-767
TL;DR: This work aims to develop an IoT system for monitoring the concentrations of indoor air pollutants using Artificial Neural Networks andGrey Relational Analysis (GRA) to select the ones which influence IAQ the most.
Abstract: Determination of Indoor Air Quality (IAQ) requires measurement and analysis of all the indoor air parameters. Often, the contribution of some parameters is substantial while that of others is meagre. This work aims to develop an IoT system for monitoring the concentrations of indoor air pollutants. Grey Relational Analysis (GRA) is then used to evaluate all the measured indoor air pollutants and select the ones which influence IAQ the most. The selected parameters are then used to build IAQ forecast model using Artificial Neural Networks (ANN). The accuracy of the ANN based forecast model is measured using mean absolute percentage error (MAPE) and coefficient of determination.
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TL;DR: From the current analysis of COVID-19 data it has been observed that trend of per day number of infection follows linearly and then increases exponentially, and the piecewise linear regression is the best suited model to adopt this property.
Abstract: Outbreak of COVID-19, created a disastrous situation in more than 200 countries around the world. Thus the prediction of the future trend of the disease in different countries can be useful for managing the outbreak. Several data driven works have been done for the prediction of COVID-19 cases and these data uses features of past data for future prediction. In this study the machine learning (ML)-guided linear regression model has been used to address the different types of COVID-19 related issues. The linear regression model has been fitted into the dataset to deal with the total number of positive cases, and the number of recoveries for different states in India such as Maharashtra, West Bengal, Kerala, Delhi and Assam. From the current analysis of COVID-19 data it has been observed that trend of per day number of infection follows linearly and then increases exponentially. This property has been incorporated into our prediction and the piecewise linear regression is the best suited model to adopt this property. The experimental results shows the superiority of the proposed scheme and to the best of our knowledge this is a new approach towards the prediction of COVID-19.
26 citations
TL;DR: The proposed Internet-of-Things (IoT) system serves to collect data, predict ventilation states, and provide alerts and recommendations to the end user, and is found to determine the poor ventilation state with accuracy, precision, recall and F1 score values.
Abstract: As the proportion of time spent by humans in indoor environment increases, it becomes challenging to maintain good air quality for healthy and productive life. The need to develop a context aware, reliable system capable of providing real time information and alerts on indoor air quality is addressed in this article. The proposed Internet-of-Things (IoT) system serves to collect data, predict ventilation states, and provide alerts and recommendations to the end user. A novel method for determination of ventilation states using three indoor pollutants PM2.5, PM10, and CO is proposed. Multilevel logistic regression is first used to define indoor ventilation states using ventilation rate which is calculated with the help of indoor CO2 concentration. $K$ -NN classification technique then predicts indoor ventilation state with the help of three input attributes, PM2.5, PM10, and CO. Context-aware information about indoor environment and current ventilation state is conveyed to the end-user in form of an alert, through a smartphone application. The system is found to determine the poor ventilation state with accuracy, precision, recall and F1 score values of 94.34%, 0.91, 0.88, and 0.89, respectively.
12 citations
TL;DR: In this article , the authors focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2).
Abstract: 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.
8 citations
TL;DR: In this article , an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) were used to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters.
Abstract: • An IoT-based framework for data collection and context modelling has been developed. • Extended Kalman Filter (EKF) approach is used to deal with inaccuracies, missing data and eliminate errors. • The EKF-derived indoor pollutant concentrations are employed in context reasoning for proposing a new index. • An adaptive neuro-fuzzy inference system (ANFIS) uses the percent of dissatisfied people (PPD), ventilation rate (VR), and AQI data to identify the present state of IAQ and the percentage of time when the air quality in the classroom is unhealthy. For a productive and healthy life, air quality plays an important role. This paper addresses the requirements to develop a system capable of providing real-time information, predictions, and alerts about the indoor environment using context-awareness. The proposed IoT system serves for data collection, pre-processing, defining rules, and forecasting the predicting states of the indoor environment by giving information to the end-user about the alerts and recommendations. A novel approach based on the indoor pollutants T, RH, CO 2 , PM 2.5 , PM 10 , and CO for the determination of the status of the environment is proposed. The pre-processing is used for filtering data using and extended Kalman filter. Further, the system uses an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters. The ANFIS model predictor considers the value of indoor pollutants to form a new index: State of indoor air (SIA). For analysis and forecasting of the new index SIA, the DTMC model is used. The collected and measured data is stored in the IoT cloud using the sensing setup, and sensed information is used to develop the SIA transfer matrix, generating return durations corresponding to each SIA and providing alerts based on the data to the end-user. The models are assessed using the expected and actual return durations. The most frequent interior ventilation states, according to the predictions, are poor and moderate. Only 0.08 percent of the time does the IAQ remain in a good state. Two-thirds of the time (66%), the indoor ventilation is severe (poor, very poor, or hazardous); 19% of the time it is very bad, and 15% of the time it is hazardous, suggesting and warning that there is a very high probability of unhealthy AQI in educational institutions in the Delhi-NCR region. Performance is measured by the comparison between actual and forecasted return periods, and the forecast error for our system is low, with an absolute forecast error of 3.47% on an average.
1 citations
References
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01 Jan 2010
525 citations
"An IoT-based System to Evaluate Ind..." refers background in this paper
...[5] have developed three systems to create a smart home environment....
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TL;DR: In this article, a combination of principal component analysis (PCA) and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API).
Abstract: This study focused on the pattern recognition of Malaysian air quality based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations in Malaysia for 7 years (2005–2011) were gathered. Principal component analysis (PCA) in the environmetric approach was used to identify the sources of pollution in the study locations. The combination of PCA and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API). The PCA has identified that CH4, NmHC, THC, O3, and PM10 are the most significant parameters. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R
2 and root mean square error (RMSE) values of 0.618 and 10.017, respectively. The work has demonstrated the importance of historical data in sampling plan strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling parameters, which in turn will reduce costs and time of sampling.
146 citations
"An IoT-based System to Evaluate Ind..." refers methods in this paper
...[11] used principal component analysis (PCA) to identify the most significant among eight air quality parameters....
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TL;DR: The proposed AOW-ELM algorithm is more effective and efficient than several state-of-the-art active learning algorithms that are specifically designed for the class imbalance scenario.
Abstract: It is well known that active learning can simultaneously improve the quality of the classification model and decrease the complexity of training instances. However, several previous studies have indicated that the performance of active learning is easily disrupted by an imbalanced data distribution. Some existing imbalanced active learning approaches also suffer from either low performance or high time consumption. To address these problems, this paper describes an efficient solution based on the extreme learning machine (ELM) classification model, called active online-weighted ELM (AOW-ELM). The main contributions of this paper include: 1) the reasons why active learning can be disrupted by an imbalanced instance distribution and its influencing factors are discussed in detail; 2) the hierarchical clustering technique is adopted to select initially labeled instances in order to avoid the missed cluster effect and cold start phenomenon as much as possible; 3) the weighted ELM (WELM) is selected as the base classifier to guarantee the impartiality of instance selection in the procedure of active learning, and an efficient online updated mode of WELM is deduced in theory; and 4) an early stopping criterion that is similar to but more flexible than the margin exhaustion criterion is presented. The experimental results on 32 binary-class data sets with different imbalance ratios demonstrate that the proposed AOW-ELM algorithm is more effective and efficient than several state-of-the-art active learning algorithms that are specifically designed for the class imbalance scenario.
98 citations
TL;DR: Based on gray correlation analysis (GCA), Ensemble Empirical Mode Decomposition (EEMD), Cuckoo search (CS) and Back-propagation artificial neutral networks (BPANN), this article proposed the CS-EEMd-BPANN model for forecasting PM concentrations.
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"An IoT-based System to Evaluate Ind..." refers methods in this paper
...[9] have used grey relational grade to investigate the relationship between transportation and ambient/roadside air pollution....
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TL;DR: A robust and efficient framework that uses dynamic time warping (DTW) as the core recognizer to perform online temporal fusion on either the raw data or the features is proposed and performance results are compared with a Hidden Markov Model (HMM) based system.
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"An IoT-based System to Evaluate Ind..." refers background in this paper
...Major indoor pollutant sources are combustion appliances, tobacco sources, insulating materials such as newly installed carpets, floorings or plasters, furniture products, pesticides, products for household cleaning and heating/humidification devices [2]....
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