Date of Award
2025
Document Type
Thesis
Degree Name
Master of Science in Operations Research (MSOR)
Department
Industrial and Systems Engineering and Engineering Management
Committee Chair
Howard Chen
Committee Member
Sean Freeman
Committee Member
Bryan Mesmer
Research Advisor
Howard Chen
Subject(s)
Meteorology in aeronautics, Weather forecasting, Machine learning, Neural networks (Computer science)
Abstract
Weather forecasting is critical to minimize risk and maximize efficiency for flying operations and is challenging due to the uncertainty involved in atmospheric changes. Operational area weather is becoming more critical with the growing reliance on air domain for transportation. This study investigates whether the forecasting accuracy using a Hybrid Long-Short Term Memory (LSTM) Artificial Neural Network (ANN) outperforms the San Antonio International Airport (KSAT) Terminal Aerodrome Forecast (TAF) by integrating diverse data sources – regional Meteorological Aerodrome Report (METAR), TAF, Avian Advisory System (AHAS) Data and geomagnetic activity K Index Data. When tested on one year of historical data, the Hybrid LSTM model exceeded the KSAT TAF’s average mean absolute error (MAE) for eleven continuous weather parameters by 64% and exceeded the accuracy for nine binary weather parameters by an average of 4%, highlighting the performance of the ANN compared to the legal forecast for KSAT.
Recommended Citation
Mote, Joshua, "Aviation weather forecasting utilizing an artificial neural network" (2025). Theses. 749.
https://louis.uah.edu/uah-theses/749