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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Spelling suggestions: "subject:"wildfire apread aprediction"" "subject:"wildfire apread iprediction""

1

Wildfire Spread Prediction Using Attention Mechanisms In U-Net

Shah, Kamen Haresh, Shah, Kamen Haresh 01 December 2022 (has links) (PDF)
An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, employing ensemble modeling reduces bias and variation, leading to more consistent and accurate predictions. When inferencing on wildfire propagation at 30-minute intervals, the architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1%.
Image Segmentation
Wildfire Spread Prediction
Google Earth Engine
Satellite Images
Deep Learning
U-Net
Attention
Ensemble Modeling
Bayesian Hyperparameter Optimization
Threshold Tuning
TensorFlow
Python
Wildfires
Artificial Intelligence and Robotics
Computational Engineering
Software Engineering

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