Post-graduate theses
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Identifier |
000463159 |
Title |
Implementation of machine learning algorithms for fire risk prediction in Greece |
Alternative Title |
Εφαρμογή αλγορίθμων μηχανικής μάθησης για την πρόβλεψη του κινδύνου πυρκαγιάς στην Ελλάδα |
Author
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Καλογεράκη, Ελένη Ε.
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Thesis advisor
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Κανακίδου, Μαρία
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Reviewer
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Βρεκούσης, Μιχαήλ
Banks, Andrew
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Abstract |
Fire prediction is an especially difficult task, as the complex interactions between the fire drivers and
conditions that start a fire can be challenging. In this work, we aim to predict fire risk with the use of ML
algorithms. Meteorological, topographical and satellite daily data in 1km x 1km grid resolution have been
utilized as parameters to train our models. The data set that was used covered the period from 2011 to
2021. The area of study comprises part of the Balkan peninsula, the whole of Greece and a part of western
Turkey.
In order to reduce the parameters set used to train the model, techniques such as Mutual Information and
Spearman Rank Correlation were applied. We employed Random Forest (RF) and XGBoost classifiers to
address a binary classification problem (fire occurrence or no fire), training our models on data from 2011-
2019 and evaluating their performance on subsequent years of 2020 and 2021.
The XGBoost model stood out for its robustness, achieving a sensitivity of 95% and specificity of 50%. Our
feature reduction analysis revealed that excluding Corine Land Cover class fractions had a negligible effect
on the model's output, whereas further reduction including normalized difference vegetation index (NDVI)
and DEM (Digital Elevation Model) variables impacted the performance slightly.
This research provides valuable insights into the application of ML algorithms for wildfire prediction. For
future investigation, incorporating numerical weather prediction (NWP) forecasting data and leveraging
unsupervised learning methods are proposed to delve deeper into the patterns and drivers of fire
incidents.
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Language |
English |
Subject |
Supervised learning |
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Wildfire prediction |
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Επιβλεπόμενη μάθηση |
Issue date |
2024-03-21 |
Collection
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School/Department--School of Sciences and Engineering--Department of Chemistry--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/e/4/0/metadata-dlib-1710233116-479560-10878.tkl
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Views |
29 |
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