Aircraft Sound Exposure Prediction With ML for Weather-Aware Noise Forecasting
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Solution Overview
Problem
Current methods for predicting aircraft noise exposure levels, such as FAA's AEDT and ANOPP, are either computationally inefficient or lack accuracy due to insufficient consideration of weather conditions and aircraft configurations, failing to provide precise noise propagation models.
Innovation Solution
Employing machine learning models trained on historical aircraft sensor data, atmospheric data, and sound data to predict sound exposure levels, utilizing feature engineering and model selection to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise prediction methods (FAA AEDT, ANOPP) are used, then computational efficiency is maintained, but prediction accuracy deteriorates due to insufficient consideration of weather conditions and aircraft configurations
Solution Approach 1:
The patent replaces traditional physics-based noise prediction models (FAA AEDT, ANOPP) with machine learning models that learn noise propagation patterns from historical data. This substitution enables the system to achieve higher prediction accuracy by capturing complex relationships between weather conditions, aircraft configurations, and noise levels without relying on computationally intensive physics simulations.
Solution Approach 2:
The patent performs preliminary training of machine learning models using extensive historical aircraft sensor data, atmospheric data, and sound data. This pre-computed knowledge is stored in the trained model parameters, allowing the system to make accurate predictions in real-time without重新 performing complex calculations during actual noise prediction operations.
2Measurement precision
If traditional noise prediction methods are used, then device complexity is reduced, but measurement precision deteriorates due to lack of comprehensive data processing
Solution Approach 1:
The patent replaces complex data processing pipelines and physics-based calculation systems with trained machine learning models. The models encapsulate the complexity of processing aircraft sensor data, atmospheric conditions, and noise measurements internally, presenting a simpler interface for prediction while maintaining high accuracy through learned patterns from comprehensive historical data.
3Measurement precision
If more comprehensive training data is used, then model accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent performs comprehensive model training using extensive historical data in advance, before deployment for actual noise predictions. This preliminary action transfers the computational burden to an offline training phase, allowing the use of large datasets and complex model architectures without impacting real-time prediction performance or operational time constraints.
Data Source
AI summary
A method, apparatus, system, and computer program product for predicting a sound exposure level generated by an aircraft. A flight path over a location is identified. The sound exposure level generated by an aircraft for the identified flight path over the location is predicted using a machine learning model trained using a training dataset comprising historical aircraft sensor data for selected parameters, historical atmospheric data, and historical sound data recorded by a microphone system for flight paths over the location.