| Summary: | 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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