Extremely low visibility affects aviation services. Aviation services need accurate fog and low-visibility predictions for airport operations. Fog and low-visibility forecasting are difficult even with modern numerical weather prediction models and guiding systems. Limitations in comprehending the micro-scale processes that lead to fog formation, intensification, onset, and dissipation complicate fog prediction. This article predicts low visibility for Jay Prakash Narayan International Airport (JPNI), Patna, India, using a historical synoptic dataset. The proposed machine learning (ML) approaches optimize three meta-algorithm approaches: boosting (which reduces variances), bagging (which reduces bias), and stacking (which improves predictive forces). The ML approaches optimize the best prediction algorithms (at level 0) for fog (surface visibility ≤ 1000 m) and dense fog (surface visibility ≤ 200 m), and the suggested ensemble models at level 1 (an ensemble of level 0 ML approaches) deliver the highest performance and stability in prediction output. All time series perform well with the specified model (6-h to 1-h lead time for any combination of observed historical datasets). Airport management, planning, and decision-making rely on high reliability. Because it works well and is reliable, the proposed approaches can be used at other airports in India's Indo-Gangetic Plain.

The aviation sector is extremely vulnerable to fog. Thus, accurate fog predictions are essential foraviation sector efficiency, particularly airport management and flight scheduling. Even with numerical weather predictionmodels and guiding systems, fog prediction is challenging. The difficulty of fog prediction is due to the inability to graspthe micro-scale factors that cause fog to form, intensify, augment and dissipate in the boundary layer. This study looks athow well machine learning (ML) tools can predict fog (Visibility <1000 m) and dense fog (Visibility <200 m) at India'sJay Prakash Narayan International Airport (ICAO Index-VEPT), a representative station of the Indo-Gangetic Plains(IGP). The proposed ensemble ML-based model was trained using hourly synoptic data from 2014 to 2020 and testedusing data from 2021 to 2022 (December to February). Once the features are chosen and the forecasters' local knowledgeis taken into account, the dry bulb temperature (°C), dew point temperature (°C), relative humidity (%), cloud amount(octa), wind direction (degrees from true north) and wind speed (knots) are used to build the proposed ML models. MLalgorithms were trained on meteorological data from 1500 to 2200 UTC to predict fog (Visibility <1000 m) and densefog (Visibility <200 m) for the next day at 0000 UTC, with a two-hour lead time. For fog forecasting at 0000 UTC with a 4-hour lead time, ML models were trained with data from 1300 to 2000 UTC and so on. This study evaluates parameter tuning in six level 0 ML models: distributed random forest (DFR), deep learning (DL), gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized tree (XRT), XG Boost and stacked ensemble at level 1. The performance metrics and statistical skill scores indicate that DRF and DL models perform well for lead times of 2 and 4 hours at level 0 for fog (visibility <1000 m) and dense fog (visibility <200 m). But the proposed ensemble models outperform all the base models at level 0 and are recognized as the best instrument for predicting fog at Patna Airport.

Fog typically results in reduced atmospheric visibility. Severely limited visibility has a significant impact on transportation, particularly the operations of aircraft. Precise forecasts of low visibility are essential for aviation services, primarily for the efficient planning of airport activities. Despite the utilization of sophisticated numerical weather prediction (NWP) models, the prediction of fog and limited visibility remains challenging. The intricacy of fog prediction is due to limitations in understanding the micro-scale factors that lead to fog genesis, intensification, persistence, and dissipation. This study investigates the occurrence of fog (surface visibility <1000 m) and dense fog (surface visibility < 200 m) throughout the climatological low-visibility months (November to February) to analyze the persistence of low-visibility events and predict them in the specific conditions of the frog prone Indo-Gangetic Plain (IGP) regions. A representative station, Jay Prakash Narayan International (JPNI) Airport in Patna, India, has been considered given the availability of instrumental quality datasets. The analysis investigates the long-term and short-term persistence and prediction of the series using a diverse variety of machine learning (ML) algorithms. To conduct a comprehensive analysis over an extended period, detrended fluctuation analysis (DFA) is employed to determine the similarities between the time series of large-scale fog and dense fog. A Markov chain model is used to look at the binary time series and figure out how long low-visibility events (like fog and dense fog) last in the short term ( 1-5 hours). Ultimately, we analyze a short-term forecast (Nowcast) with a lead time of one to five hours for instances of low visibility (fog or dense fog). This nowcasting is generated utilizing diverse methodologies, including Markov chain models, persistence analysis, and machine learning (ML) methods. Finally, establish that the most favorable and reliable results in this prediction problem are attained by employing a Mixture of Experts model that integrates persistence-based methods and ML algorithms.