Airport visibility prediction method, storage medium and related device

By extracting differentiated features from numerical meteorological data and training a gradient boosting decision tree model, the problems of insufficient feature extraction accuracy and slow model convergence speed in existing technologies are solved, achieving efficient and accurate results for airport visibility prediction.

CN121980146APending Publication Date: 2026-05-05中国民用航空珠海空中交通管理站
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Patent Information

Application Number
CN202610081700.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-21
Publication Date
2026-05-05

AI Technical Summary

Technical Problem

Existing airport visibility prediction methods suffer from insufficient feature extraction accuracy and slow model convergence, resulting in inconsistent prediction accuracy and low efficiency.

Method used

A differentiated feature extraction method is adopted to extract two-dimensional spatial correlation features and one-dimensional temporal evolution features from numerical meteorological data. The model is then trained using a gradient boosting decision tree model to optimize feature weights and model parameters, thereby improving the model's convergence speed and prediction accuracy.

Benefits of technology

It significantly improves the accuracy and efficiency of visibility prediction, accelerates model convergence, makes visibility prediction results more reliable, and provides efficient decision support.

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Abstract

The invention relates to the technical field of airport meteorological element prediction, and discloses an airport visibility prediction method, a storage medium and a related device. The method comprises the following steps: acquiring numerical weather forecast data and weather live data of an airport which take effect in the airport and nearby areas; respectively extracting data associated with visibility from the numerical weather forecast data and the weather live data as numerical weather factor forecast data and weather factor live data; performing two-dimensional extraction on grid type data in the numerical meteorological element forecast data and the meteorological element live data to obtain a first spatial correlation feature, and performing one-dimensional extraction on single-point time sequence meteorological data in the numerical meteorological element forecast data and the meteorological element live data to obtain a first time evolution feature; and inputting the first space correlation feature and the first time evolution feature into a pre-trained visibility prediction model to obtain a visibility short-term prediction result and an approaching prediction result. The method can improve the accuracy and efficiency of visibility prediction.
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