Flight delay stacking ensemble prediction model for severe weather
Weather factors, as the primary factors affecting flight delays, have an important impact on flight delay prediction. Confronting the severe weather, multi-classification prediction of flight delay duration was made, and a Stacking-based integrated flight delay prediction model was proposed for the...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2025-03-01
|
| Series: | 大数据 |
| Subjects: | |
| Online Access: | http://www.j-bigdataresearch.com.cn/zh/article/doi/10.11959/j.issn.2096-0271.2025012/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850207962551812096 |
|---|---|
| author | SUN Yue DING Jianli |
| author_facet | SUN Yue DING Jianli |
| author_sort | SUN Yue |
| collection | DOAJ |
| description | Weather factors, as the primary factors affecting flight delays, have an important impact on flight delay prediction. Confronting the severe weather, multi-classification prediction of flight delay duration was made, and a Stacking-based integrated flight delay prediction model was proposed for the problems of low prediction accuracy and poor stability of traditional single model. Combining flight data and weather data features, multiple heterogeneous classifiers such as LightGBM and XGBoost were used as base learners, and SVM was used as the primary learner. A stacked, two-layer integrated learning framework was constructed. To verify the model validity, multiple single models were constructed for comparison with the integrated model. The experimental results demonstrate that the Stacking integrated prediction model has the best performance with an overall accuracy of 95.25% and an F1 score of 0.9527. |
| format | Article |
| id | doaj-art-900e006a0d094bac9dc0e70b87dc3646 |
| institution | OA Journals |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-900e006a0d094bac9dc0e70b87dc36462025-08-20T02:10:20ZzhoChina InfoCom Media Group大数据2096-02712025-03-011115216686967538Flight delay stacking ensemble prediction model for severe weatherSUN YueDING JianliWeather factors, as the primary factors affecting flight delays, have an important impact on flight delay prediction. Confronting the severe weather, multi-classification prediction of flight delay duration was made, and a Stacking-based integrated flight delay prediction model was proposed for the problems of low prediction accuracy and poor stability of traditional single model. Combining flight data and weather data features, multiple heterogeneous classifiers such as LightGBM and XGBoost were used as base learners, and SVM was used as the primary learner. A stacked, two-layer integrated learning framework was constructed. To verify the model validity, multiple single models were constructed for comparison with the integrated model. The experimental results demonstrate that the Stacking integrated prediction model has the best performance with an overall accuracy of 95.25% and an F1 score of 0.9527.http://www.j-bigdataresearch.com.cn/zh/article/doi/10.11959/j.issn.2096-0271.2025012/Stacking ensemble learningmulti model fusionsevere weather |
| spellingShingle | SUN Yue DING Jianli Flight delay stacking ensemble prediction model for severe weather 大数据 Stacking ensemble learning multi model fusion severe weather |
| title | Flight delay stacking ensemble prediction model for severe weather |
| title_full | Flight delay stacking ensemble prediction model for severe weather |
| title_fullStr | Flight delay stacking ensemble prediction model for severe weather |
| title_full_unstemmed | Flight delay stacking ensemble prediction model for severe weather |
| title_short | Flight delay stacking ensemble prediction model for severe weather |
| title_sort | flight delay stacking ensemble prediction model for severe weather |
| topic | Stacking ensemble learning multi model fusion severe weather |
| url | http://www.j-bigdataresearch.com.cn/zh/article/doi/10.11959/j.issn.2096-0271.2025012/ |
| work_keys_str_mv | AT sunyue flightdelaystackingensemblepredictionmodelforsevereweather AT dingjianli flightdelaystackingensemblepredictionmodelforsevereweather |