Abstract The aerosol particles present in the atmospheric region mainly affect the climate radiative forcing directly by scattering & absorbing the sunlight. Also, it indirectly influences the formation of clouds, precipitation and acts as a considerable uncertainty in assessing Earth's radiation budget. Determination of aerosol type is significant in characterizing the aerosol role in the atmospheric processes, feedback, and climate models. This paper proposes two aerosol classification models, one based on the source and another based on the composition, to classify the aerosols using aerosol optical properties. The source based aerosol classification method helps to identify the sources which cause pollution in a particular region. Based on the results, proper control measures can be taken to reduce pollution. The composition based aerosol classification helps to identify the nature of aerosol types, such as absorbing or non-absorbing. This classification helps to study the climate of the Kanpur region. The aerosol data is taken from AERONET (AErosol RObotic NETwork) for the period 2002–2018 for the Kanpur region. The composition based aerosol classification model uses Single Scattering Albedo (SSA), Angstrom Exponent (AE), and Fine Mode Fraction (FMF) parameters to categorize aerosols based on their composition. The source based aerosol classification model classifies the aerosols based on values of AE and Aerosol Optical Depth (AOD) and describes the source of the aerosol particles. Knowledge of aerosol sources and compositions helps execute policies or controls to reduce aerosol concentrations. Machine learning algorithms, Naïve Bayes, K Nearest Neighbor, Decision Tree, Support Vector Machine, and Random Forest are used to validate classification schemes. The performance analysis of machine learning algorithms is compared using ten different metrics, and the results are also compared with the existing aerosol classification models. The results of the classification show that the source based aerosols of the desert and arid background and the composition based aerosols of types, Mixture Absorbing, Coarse absorbing (Dust), and Black Carbon are dominant over the Kanpur region during the study period considered. The Number of non-absorbing (scattering) type aerosols are least in the study region considered during the study period at all the seasons. It is found that the Random Forest and Decision Tree models outperform the other machine learning models considered and the existing classification models in terms of accuracy (99.55 %) and other performance metrics considered.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Composition and source based aerosol classification using machine learning algorithms


    Beteiligte:
    Annapurna, S.M (Autor:in) / Anitha, M. (Autor:in) / Sutha Kumar, Lakshmi (Autor:in)

    Erschienen in:

    Advances in Space Research ; 73 , 1 ; 474-497


    Erscheinungsdatum :

    2023-09-30


    Format / Umfang :

    24 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Driver Classification of Shifting Strategies Using Machine Learning Algorithms

    Ebel, André / Trost, Daniel / Brosch, Erwin et al. | SAE Technical Papers | 2020


    Driver Classification of Shifting Strategies Using Machine Learning Algorithms

    Trost, Daniel / Ebel, André / Brosch, Erwin et al. | British Library Conference Proceedings | 2020


    Road marking detection and classification using machine learning algorithms

    Chen, Tairui / Chen, Zhilu / Shi, Quan et al. | IEEE | 2015


    Driver Classification of Shifting Strategies Using Machine Learning Algorithms

    Trost, Daniel / Ebel, André / Brosch, Erwin et al. | British Library Conference Proceedings | 2020


    Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

    Zaide Duran / Kubra Ozcan / Muhammed Enes Atik | DOAJ | 2021

    Freier Zugriff