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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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




    Publication date :

    2024-04-25


    Size :

    2285110 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Subang Conference by Asean Port Association

    British Library Online Contents | 1998


    Visibility of airport runways

    Barr, N.L. / Hussman, Jr., T.A. / Parker, Jr., J.F. | Engineering Index Backfile | 1956



    THE VISIBILITY OF AIRPORT RUNWAYS1

    PARKER, JAMES F. / BARR, NORMAN LEE / HUSSMAN, THOMAS A. | SAE Technical Papers | 1956