FLIGHT DATA OF AIRPLANE FOR WIND FORECASTING

被引：0

作者：

Sharma, Astha ^{[1
]}

Hoque, Md Tamjidul ^{[1
,2
]}

Ioup, Elias ^{[3
]}

Abdelguerfi, Mandi ^{[1
,2
]}

机构：

[1] Univ New Orleans, Dept Comp Sci, New Orleans, LA 70148 USA

[2] Univ New Orleans, Canizaro Livingston Gulf States Ctr Environm Info, New Orleans, LA 70148 USA

[3] Naval Res Lab, Ctr Geospatial Sci, Stennis Space Ctr, MS USA

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

Machine Learning; Weather Forecasting; Genetic Algorithm; kNN imputation; Linear Regression; Extreme Gradient Boosting; Sliding Window;

D O I：

10.1109/IGARSS39084.2020.9323718

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Understanding and predicting weather behavior is vital for informing pilots about changing flight conditions. This paper presents a new approach towards forecasting one component of weather information, wind speed, from data captured by airplanes in flight. We compare two datasets for prediction suitability, and a collinearity analysis between these datasets reveals a better model performance with smaller test error with one of them. We then apply machine learning and a genetic algorithm to process this data further and arrive at a competitive error rate. Finally, we create an offline software for wind prediction using the best performing classifier.

引用

页码：1853 / 1856

页数：4

共 7 条

[1]

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[2]

Federal Aviation Administration (FAA)/Aviation Supplies & Academics (ASA), 2010, 0045G FAAASA

[3]

Kapoor A, 2014, PROCEEDINGS OF THE 13TH INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN' 14), P25, DOI 10.1109/IPSN.2014.6846738

[4]

National Oceanic and Atmospheric Administration, AIRCR DAT WEB

[5]

National Oceanic and Atmospheric Administration, 2019, WIND TEMP FOR

[6]

Roberts C., 2019, AM HAS GOTTEN BAD PR

[7]

YANG M., 2018, ORNL DAAC

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