Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa

Abstract

The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not exist. Here in Africa, dirty air is brought about by the growth in industrialization, urbanization, flights, and road traffic. Air pollution remains such a silent killer, especially in Africa, and if not dealt with, it will continue to lead to health issues, such as heart conditions, stroke, and chronic respiratory organ unwellness, which later result in death. In this paper, the Kampala Air Quality Index prediction model based on the fuzzy logic inference system was designed to determine the air quality for Kampala city, according to the air pollutant concentrations (nitrogen dioxide, sulphur dioxide and fine particulate matter 2.5). It is observed that fuzzy logic algorithms are capable of determining the air quality index and therefore, can be used to predict and estimate the air quality index in real time, based on the given air pollutant concentrations. Hence, this can reduce the effects of air pollution on both humans and the environment.

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

“…Very few of the proposed weather stations in the literature had achieved the guarantee of a quick and easy weather update, due to the lack of real-time data acquisition. Furthermore, the majority of the designs are simulation-based and did not provide all the necessary information under real conditions (Dionova et al 2020 ; Ghorbani and Zamanifar 2022 ; Katushabe et al 2021 ).…”

“…However, in the case where the membership of certain elements is unclear, classical logic becomes incapable to deal with complex real-life problems that contain some degrees of ambiguity. Therefore, fuzzy logic is used to address fuzziness in real-world problem-solving by assigning the meaning to the values ranging between 0 and 1 (Katushabe et al 2021 ).…”

“…The categorization levels of the air quality metrics are shown in Tables 2 and 3 , which are mainly based on reported works (Carbajal-Hernández et al 2012 ; Dionova et al 2020 ; EPA 2014 ; Katushabe et al 2021 ).…”

Adoption of Artificial Intelligence in the Ugandan Health Sector: a review of Literature

“…Bisaso et al [31] Machine Learning A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients Nshimiyimana and Zhou [32] Machine Learning Analysis of risk factors associated with acute respiratory infections among under-five children in Uganda. Katushabe et al [33] Fuzzy Logic Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa Finnegan et al [34] Machine Learning Deploying machine learning with messy, real-world data in low-and middle-income countries: Developing a global health use case Ouma et al [35] Statistical Modelling Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda Mafigiri et al [36] Statistical Modelling HIV prevalence and uptake of HIV/AIDS services among youths (15-24 Years) in fishing and neighbouring communities of Kasensero, Rakai District, South Western Uganda Igulot [37] Statistical Modelling Sexual and Gender-Based Violence and Vulnerability to HIV Infection in Uganda: Evidence from Multilevel Modelling of Population-Level HIV/AIDS Data Kabukye et al [38] Statistical Modelling Assessment of organizational readiness to implement an electronic health record system in a low-resource settings cancer hospital: A cross-sectional survey Bbosa et al [39] Machine Learning On the goodness of fit of parametric and nonparametric data mining techniques: the case of malaria incidence thresholds in Uganda Roberts and Matthews [40] Statistical Modelling Risk factors of malaria in children under the age of five years old in Uganda Nabyonga et al [41] Statistical Modelling Health care seeking patterns and determinants of outof-pocket expenditure for Malaria for the children under-five in Uganda Baik et al [42] Statistical Modelling A clinical score for identifying active tuberculosis while awaiting microbiological results: Development and validation of a multivariable prediction model in sub-Saharan Africa Becker et al [17] Deep Learning Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study Coker et al [15] Machine Learning A land use regression model using machine learning and locally developed low-cost particulate matter sensors in Uganda. Muyama et al [43] Deep Learning Automated Detection of Tuberculosis from Sputum Smear Microscopic Images Using Transfer Learning Techniques.…”

Internet of things based visualisation of effect of air pollution on the lungs using HEPA filters air cleaner

“…According Global updates on air pollution 2021 [2] , air pollution concentrations still exceed limit the levels and has greatly worsened mostly in the most parts of low- and middle-income countries due to increased urbanisation, industrialization, traffic and scaled up economic development that increase burning of fossil fuels [3] .…”