Breathable Cities: Dynamic Machine Learning Modelling Approaches for Advanced Air Pollution Control

Abstract

first_pageDownload PDFsettingsOrder Article Reprints Open AccessArticle Breathable Cities: Dynamic Machine Learning Modelling Approaches for Advanced Air Pollution Control by Roba Zayed andMaysam Abbod *ORCID Department of Electronic and Electrical Engineering, Brunel University London, London UB8 3PH, UK * Author to whom correspondence should be addressed. Appl. Sci. 2024, 14(13), 5581; https://doi.org/10.3390/app14135581 Submission received: 20 May 2024 / Revised: 19 June 2024 / Accepted: 24 June 2024 / Published: 27 June 2024 (This article belongs to the Special Issue Air Quality Prediction Based on Machine Learning Algorithms II) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract This paper discusses air quality index (AQI) representation using a fuzzy logic framework to cover the blurry areas of AQI where indices are in between ranges of values. After studying several standards for air quality prediction (AQP), this research suggested the use of fuzzy logic as an extended method to cover some limitations found in several standards, in which the fuzzy logic represents a more dynamic way to support cross-country comparisons as well. This research expanded upon the United States Environmental Protection Agency (USEPA) standards to address their acknowledged limitations by constructing a fuzzy air quality levels prediction (FAQLP) model, which categorizes air quality into corresponding ranges (actual levels) and classifies new fuzzy levels (predicted levels), using a fuzzy logic model (to enforce more realistic predictions). This model can solve the issue of values at or near boundaries when there is uncertainty about air quality levels. The study aims to incorporate a comparative study of two urban settings providing dynamic machine-learning modeling approaches for advanced air pollution control. The DNN–Markov model is presented in this paper as the selected hybrid model for AQI prediction, and the adaptive neuro-fuzzy inference system (ANFIS) was used to represent AQI. This work presents a novel air quality index framework that consists of a DNN–Markov model for accurate hourly predictions and air quality level representations using ANFIS.