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

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 ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</tex> ) 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 ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</tex> ) 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</tex> values. The outcomes of this study are poised to yield practical implications, particularly in refining visibility prognostications and mitigating the likelihood of aviation incidents.