A Smart City Air Quality Data Imputation Method Using Markov Weights-Based Fuzzy Transfer Learning

Abstract

An intelligent air quality monitoring system (IAQMS) is one of the key aspects of any smart city. The success of these monitoring systems largely depends on the quality of data. Missing air quality data is one of the crucial issues in any IAQMS, especially in small cities where sufficient historical air quality data is not available. In this study, a fuzzy transfer learning-based imputation (FTLI) method is proposed for the smart imputation of missing air quality data. The central concept of this proposed method is to acquire knowledge through fuzzy inference systems from other air quality monitoring systems called source domains where sufficient data is available. Later that knowledge is applied to impute the missing values of the target IAQMS (target domain) through some knowledge adaptation techniques. In this proposed method, Markov weights (transition probability matrix) are used to impute the missing values more accurately. The proposed imputation method is tested on the various missing data situations, which include random missing values, continuous missing values, and high missing rates. In this study, the performance of the proposed method is compared with other well-established methods, and the comparison results exhibit that the FTLI method outruns other methods under different missing rates.

KEYWORDS:

• Air quality monitoring
• Fuzzy logic
• Imputation
• Markov weight
• Missing data
• Transfer learning

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Samit Bhanja

Samit Bhanja is a PhD scholar with the Department of Computer Science & Engineering at Aliah University, Kolkata. His research area is machine learning and deep learning. He is currently working as an assistant professor, Department of Computer Science, Government General Degree College, Singur, Hooghly. He has an MTech in computer science & engineering from the Maulana Abul KalamAzad University of Technology (formerly the West Bengal University of Technology). Email: [email protected]

Santanu Metia

Santanu Metia received the BE degree in electrical engineering from the Jalpaiguri Government Engineering College, India, in 1997, the MS degree in electrical engineering from the Indian Institute of Technology Kharagpur, India, in 2002, and the PhD degree from the University of Technology Sydney, Australia, in 2016. He is currently working as a research associate at the University of Technology Sydney, Australia. His research interests include Kalman filtering, fractional systems, indoor air quality modeling, climate change, and air quality modeling. Email: [email protected]

Abhishek Das

Abhishek Das is currently working as an associate professor in the Department of Computer Science and Engineering at Aliah University, Kolkata. He has over 16 years of Teaching, Research as an ex-reader at the Indian Institute of Space Sc. & Technology (IIST), ex-asst professor at Tripura University (Central University) and ex-lecturer at BESU Shibpur. He also worked at AICTE (under MHRD) as asst director and Regional Officer. He also has a Post Doc from the University of West Scotland, UK. He received his PhD from Jadavpur University, India, MS from Kansas State University, USA, and BTech from Kalyani University, India. His research area is in medical image processing, machine learning, IoT, information security, etc.