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.