• Login
    • University Home
    • Library Home
    • Lib Catalogue
    • Advance Search
    View Item 
    •   IR@KDU Home
    • ACADEMIC JOURNALS
    • International Journal of Research in Computing
    • Volume 01 , Issue 01, 2022
    • View Item
    •   IR@KDU Home
    • ACADEMIC JOURNALS
    • International Journal of Research in Computing
    • Volume 01 , Issue 01, 2022
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Prediction of Air Quality Index in Colombo

    Thumbnail
    View/Open
    14-20.pdf (977.0Kb)
    Date
    2022-01-10
    Author
    Fernando, RM
    Ilmini, WMKS
    Vidanagama, DU
    Metadata
    Show full item record
    Abstract
    Air is always considered as the main critical factor on which human survival depends on. The AQI or long firmly air quality index is the index value that illustrates qualitatively the current state of the air. The substantial AQI will further menace the living creatures’ health & the living atmosphere. Terrible air quality has been a major concern in Sri Lanka, particularly in urban cities such as Colombo and Kandy. Reliable AQI prediction will assist to decrease the health risks caused by air pollution. The goal of this study has been to find the most suitable machine learning approach for predicting accurate air quality index in Colombo based upon PM2.5 particular concentration. In this study, PM2.5 concentration in Colombo had been predicted using four correlated air pollutant concentrations such as SO2, NO2, PM2.5, & PM10. The obtained dataset was pre-processed via prediction in order to improve prediction accuracy. The gathered dataset Cross-validated as according to 80% for training & 20% for testing the prediction model. Machine learning methods such as K-Nearest Neighboring, Multiple Linear-Regression, Random Forest, and Support Vector Machines were used to train and evaluate the prediction models. In the end, we achieved 83.25% accuracy for the K-Nearest Neighboring algorithm model, 84.68% accuracy for the Support Vector Machines model, 85.17% accuracy for the Random Forest model, and 41.9% accuracy for the Multiple Regression Model. Random Forest was recognized as the best appropriate prediction model after evaluating the models, with over 85% greater accuracy.
    URI
    http://ir.kdu.ac.lk/handle/345/5301
    Collections
    • Volume 01 , Issue 01, 2022 [8]

    Library copyright © 2017  General Sir John Kotelawala Defence University, Sri Lanka
    Contact Us | Send Feedback
     

     

    Browse

    All of IR@KDUCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsFacultyDocument TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsFacultyDocument Type

    My Account

    LoginRegister

    Library copyright © 2017  General Sir John Kotelawala Defence University, Sri Lanka
    Contact Us | Send Feedback