A Deep Learning Approach for Predicting Aerial Suppressant Drops in Wildland Firefighting Using Automatic Dependent Surveillance–Broadcast Data

This study utilizes Automatic Dependent Surveillance–Broadcast (ADS-B) data sourced by the OpenSky Network to curate a dataset aimed at enhancing the precision of aerial suppressant drop predictions in wildland firefighting. By amalgamating ADS-B data with Automated Telemetry Unit (ATU) drop informa...

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Vydané v:Fire (Basel, Switzerland) Ročník 7; číslo 11; s. 380
Hlavní autori: Magstadt, Shayne, Wei, Yu, Pietruszka, Bradley M., Calkin, David E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.11.2024
Predmet:
ADS-B data
ADS-B system
aerial firefighting
Aircraft
Aircraft configurations
Aircraft performance
Airtankers (Forest fire control)
Altitude
Artificial intelligence
Artificial neural networks
Automation
Aviation
Comparative analysis
Datasets
Decision making
Deep learning
Fire extinction
Fire fighting
Fixed wings
Location
Long short-term memory
Machine learning
Neural networks
Predictions
Real time operation
real-time tracking
Situational awareness
Spatial memory
suppressant drop prediction
Surveillance
Technology application
Telemetry
Wildfires
United States
United States > US
ISSN:2571-6255, 2571-6255
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Popis
Shrnutí:This study utilizes Automatic Dependent Surveillance–Broadcast (ADS-B) data sourced by the OpenSky Network to curate a dataset aimed at enhancing the precision of aerial suppressant drop predictions in wildland firefighting. By amalgamating ADS-B data with Automated Telemetry Unit (ATU) drop information, this research constructs a reliable base for analyzing the spatial aspects of aerial firefighting operations. Using sequential machine learning models, specifically Long Short-Term Memory (LSTM) networks and 1D Convolutional Neural Networks (1DCNN), the study interprets complex flight dynamics to predict drop locations. The dataset, covering 2017 to 2023, is labeled and segmented to reflect accurate suppressant release events, facilitating the distinction between drop and non-drop activities in fixed-wing aircraft. The LSTM model demonstrated strong predictive performance with an F1 score of 0.922, effectively identifying suppressant drop events with high accuracy. This model’s reliable predictions can significantly improve situational awareness in real-time aerial firefighting operations, enabling more informed decision-making and better coordination of resources during wildfire events.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2571-6255
2571-6255
DOI:10.3390/fire7110380