Detection of anomalous nitrogen dioxide (NO<sub>2</sub>) concentration in urban air of India using proximity and clustering methods

Aggarwal, Apeksha; Toshniwal, Durga

doi:10.1080/10962247.2019.1577314

Cited by 18 publications

(15 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the results also suggested the co-occurrence of high NO2 with average PM2.5 and average SO2 {'pm2', 'no23', 'so22'}. A detailed analysis of reasons for high concentration of NO2 in several parts of India is given in [37]. Several studies suggested hotspots for increasing SO2 and NO2 [38] in several locations of India along with PM2.5 justifying itemset {'pm2', 'no22', 'so22'} to be frequent with medium concentrations of each of these pollutants.…”

Section: H Discussionmentioning

confidence: 97%

See 1 more Smart Citation

Frequent Pattern Mining on Time and Location Aware Air Quality Data

Aggarwal

Toshniwal

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

With the advent of big data era, enormous volumes of data are generated every second. Varied data processing algorithms and architectures have been proposed in the past to achieve better execution of data mining algorithms. One such algorithm is extracting most frequently occurring patterns from the transactional database. Dependency of transactions on time and location further makes frequent itemset mining task more complex. The present work targets to identify and extract the frequent patterns from such time and location-aware transactional data. Primarily, the spatio-temporal dependency of air quality data is leveraged to find out frequently co-occurring pollutants over several locations of Delhi, the capital city of India. Varied approaches have been proposed in the past to extract frequent patterns efficiently, but this work suggests a generalized approach that can be applied to any numeric spatio-temporal transactional data, including air quality data. Furthermore, a comprehensive description of the algorithm along with a sample running example on air quality dataset is shown in this work. A detailed experimental evaluation is carried out on the synthetically generated datasets, benchmark datasets, and real world datasets. Furthermore, a comparison with spatio-temporal apriori as well as the other state-of-the-art non-apriori-based algorithms is shown. Results suggest that the proposed algorithm outperformed the existing approaches in terms of execution time of algorithm and memory resources.INDEX TERMS Air quality, data mining, frequent, itemset, spatio-temporal.

show abstract

Section: H Discussionmentioning

confidence: 97%

“…suffer from high pollution levels usually. Location of Punjabi bagh (S3,*) is surrounded by industrial areas from various sides [37]. Hence, the presence of 'so23' and 'no23' in their itemsets is more frequent than other spatial locations.…”

Section: H Discussionmentioning

confidence: 99%

Frequent Pattern Mining on Time and Location Aware Air Quality Data

Aggarwal

Toshniwal

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method included a clustering algorithm and a mean shift-based algorithm to aggregate reported anomalies on data to the server. Aggarwal et al [22] proposed a hybrid of proximity-based and clustering-based anomaly detection approaches to identify anomalies in the air quality data. The Gaussian distribution property of the real-world data set was further utilized to segregate out anomalies.…”

Section: Clustering-based Methodsmentioning

confidence: 99%

Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Liu

2021

Electronics

View full text Add to dashboard Cite

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

show abstract

“…Similarly, simulation and data mining are well-known modeling tools and techniques for predicting and assessing air quality. In this context, Aggarwal et al (2019) and Bai et al (2017) have concentrated on the models used to predict the abnormality exploration in air quality. Deep learning applications (as a subset of machine learning) have recently shown considerable potential for investigating further aspects of the ecological dimensions (Christin et al, 2019;Fairbrass et al, 2019;and Torney et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the size and complexity of big data in air quality systems, the essential for soft computing approaches have extensively increased, particularly with the growing interests in the systems of early warning alerts and preventive actions for pollutants' when high concentrations of pollutants are observed (Taylan, 2017). Recently, several attempts have been conducted to investigate air quality using machine-learning and neuro-fuzzy (ANFIS) approaches and big data analytics (Masmoudi et al, 2020;Sharma et al, 2020;Aggarwal et al, 2019;Macing et al, 2019;Sayeed et al, 2019;Bai et al, 2017;Pan et al, 2017;Wang et al, 2017;Prasad et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches

Taylan

Alkabaa

Alamoudi

et al. 2021

Preprint

View full text Add to dashboard Cite

Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision making. Several important pollution factors, such as SO2, CO, PM10, O3, NOx, H2S, location, and many others, have detrimental effects on air quality. Air quality cannot be precisely recorded and measured due to the total effect of pollutants that usually cannot be collectively prescribed by a numerical value. Therefore, evolution is required to take into account the complex, poorly defined air quality problems in which several naive and noble modeling approaches are used to evaluate and solve. In this study, hybrid data-driven machine learning, and neuro-fuzzy methods are integrated for estimating the air quality in the urban area for public health concerns. 1771 data are collected during three years for each pollution factor, starting from June 1, 2016, till September 30, 2019. The Back-Propagation Multi-Layer Perceptron (BPMLP) algorithm was employed with the steepest descent approach to reduce the mean square error for training the algorithm of the neuro-fuzzy model. Levenberg-Marquardt (LM) approach was also employed as an optimization method with Artificial neural networks (ANNs) for solving nonlinear least-squares problems in this study. These approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of soft computing intelligent models, the public's early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed remarkable performance of ANFIS and ANN-based Air Quality models for High dimensional data assessment.

show abstract

Detection of anomalous nitrogen dioxide (NO₂) concentration in urban air of India using proximity and clustering methods

Cited by 18 publications

References 54 publications

Frequent Pattern Mining on Time and Location Aware Air Quality Data

Frequent Pattern Mining on Time and Location Aware Air Quality Data

Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches

Contact Info

Product

Resources

About

Detection of anomalous nitrogen dioxide (NO2) concentration in urban air of India using proximity and clustering methods

Cited by 18 publications

References 54 publications

Frequent Pattern Mining on Time and Location Aware Air Quality Data

Frequent Pattern Mining on Time and Location Aware Air Quality Data

Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches

Contact Info

Product

Resources

About

Detection of anomalous nitrogen dioxide (NO₂) concentration in urban air of India using proximity and clustering methods