Fog prediction using artificial intelligence: A case study in Wamena Airport

Fog is one of the atmospheric phenomena that affect airport operations. It can reduce visibility which impacts flight operations (taxiing, take-off, landing). Therefore, fog prediction is needed to support flight safety. The biggest challenge in making weather predictions is the chaotic and complicated process of the atmosphere. This research tries to use artificial intelligence (AI) to predict fog events at Wamena Airport. Design of model prediction using hourly synoptic data set from January 2015 till May 2018. Variables input such as dry ball temperature, wet ball temperature, dew point, relative humidity, cloud cover, wind direction, wind speed, visibility, and present weather for the past six hours ago are used to predict fog or no fog events. We performed a grid search parameter tuning on five algorithms such as Distributed Random Forest (DFR), Deep Learning (DL), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), and Extreme Randomized Tree (XRT). The best model is obtained from the ensemble model Stacked Ensemble (SE) with an accuracy of above 90% for the fog forecast from one to three hours later.