为减少低能见度造成的航班备降和返航，利用地面观测数据和 ECMWF 高空、地面数值预报产品，基于机器学习算法建立景德镇机场当前和未来1 h低能见度短临预报模型。通过对比检验发现XG‐Boost和LightGBM机器学习算法在机场低能见度短临预报中优于SVM机器学习算法，特征筛选对XG‐Boost和LightGBM机器学习算法的性能都有改善；通过SHAP方法解释LightGBM机器学习模型，分析各特征对模型输出的贡献。主要结论如下：（1）LightGBM 和 XGBoost建立的机器学习模型在机场低能见度预报方面表现良好，AUC可达0. 98，对于当前低能见度和未来1 h低能见度预报的F1_score最高可达 0. 92。（2）基于“机器学习特征工程要求特征相互独立”原理对特征进行清洗筛选，有利于提高 XG‐Boost算法模型对未来1 h低能见度的预报准确率，而经过特征筛选的LightGBM模型在预报当前和未来1 h低能见度时比没有特征筛选的LightGBM模型漏报率更低。对当前低能见度的预报，LightGBM_24_0h模型最优，对未来 1 h低能见度的预报，XGBoost_24_1h模型最优，且特征筛选对 XGBoost算法的性能提升更大。（3）分别使用分裂次数和SHAP值分析LightGBM算法模型的特征重要性，表明在不同特征重要性准则下，机场实测相对湿度、气温、风、海平面气压和ECMWF预报的1000 hPa相对湿度、925 hPa垂直速度和散度、850 hPa散度9个特征对机场低能见度的预报更重要，且散度作为机器学习模型的输入特征可以极大提高机器学习模型的性能。（4）基于SHAP值解释特征重要性时，排名前十的特征重要性累计占比80%，说明在以雾为主的景德镇机场低能见度短临预报中LightGBM模型能根据关键预报因子输出预测结果，且在预报未来 1 h低能见度是否持续时，可重点关注 850 hPa散度、1000 hPa相对湿度、机场海平面气压和风向的变化。

In order to reduce the rate of flight diversion and return caused by low visibility，this study has established a short-term and nowcasting model of low visibility using ground observation data and the upper-air and surface forecast data of the ECMWF at Jingdezhen Airport based on machine learning algorithms. Comparing the evaluation indicators，the results find that the XGBoost and LightGBM machine learning algorithms outperform the SVM machine learning algorithm in nowcasting of the airport low visibility. A detailed comparison of the evaluation metrics was conducted both before and after feature screening in the same machine learning algorithms. The study highlights that feature screening significantly boosts the effectiveness of both models. Further‐ more，the SHAP（SHapley Additive exPlanations）method elucidates the contribution of each feature to the LightGBM model's output. The main conclusions are as follows：（1）The machine learning models established by LightGBM and XGBoost perform well in airport low visibility forecasting，with the AUC reaching up to 0. 98，and the F1_score for the prediction of current low visibility and the low visibility in the next one hour can reach up to 0. 92.（2）Data cleaning and feature screening is conducive to improving the prediction accuracy of the XGBoost algorithm for low visibility in the next hour，according to the principle that "feature engineering in machine learning requires features to be mutually independent". Moreover the LightGBM model with feature screening has a lower false negative rate than the LightGBM model without feature selection when forecasting the current and future one-hour low visibility. For the forecast of the current low visibility，the LightGBM_24_0h model is the best. For the forecast of low visibility in the next one hour，the XGBoost_24_1h model is the best. And feature selection has a greater improvement on the performance of the XGBoost algorithm.（3）The splitting times and SHAP values are used respectively to analyze the feature importance of the LightGBM algorithm mod‐ el. It shows that under different feature importance criteria，nine features，namely the measured relative humidity，air temperature，wind，sea level pressure at the airport，and the relative humidity at 1000 hPa，vertical velocity and divergence at 925 hPa，and divergence at 850 hPa predicted by ECMWF，are more important for the pre‐ diction of low visibility at the airport. And divergence，as an input feature of the machine learning model，can greatly improve the performance of the machine learning model.（4）When explaining feature importance based on SHAP values，the cumulative proportion of the top ten feature importance accounts for 80%. This indicates that in the nowcasting of low visibility at Jingdezhen Airport where fog is the main factor，the LightGBM model can output prediction results according to key forecast factors，. And when forecasting whether the low visibility in the next one hour will continue，more attention should be paid to the changes in 850 hPa divergence， 1000 hPa relative humidity，airport sea level pressure and wind direction.