Air quality management using genetic algorithm based heuristic fuzzy time series model

Abstract

Purpose: The purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality service quality air quality etc. Design/methodology/approach: In this paper a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules. Findings: The proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results it is observed that the proposed model performs better than the existing models. Practical implications: The management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place. Originality/value: The proposed method is an improved version of the adaptive time-variant FTS model. Further a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals. © 2023 Elsevier B.V. All rights reserved.