In the context of aviation, the anticipation of visibility is contingent upon the consideration of diverse meteorological factors. This study systematically examines the influence of the cross-validation technique ($k$) on the precision of visibility predictions, as gauged by root mean square error and mean absolute error. Employing the Regression Learner, encompassing 26 predetermined algorithms, and employing cross-validation ($k$) iterations ranging from 5 to 15, the primary objective was to discern the optimal model for visibility prognosis. Notably, our analysis extends to two distinct airports in Peninsular Malaysia, thereby enabling a comparative assessment. Results elucidate that the Gaussian Process Regression model consistently demonstrates superior efficacy across varied meteorological parameters and diverse $k$ values. The outcomes of this study are poised to yield practical implications, particularly in refining visibility prognostications and mitigating the likelihood of aviation incidents.


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    Titel :

    Utilizing Advanced Regression Techniques to Forecast Visibility at Subang and Langkawi International Airport




    Erscheinungsdatum :

    25.04.2024


    Format / Umfang :

    2285110 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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