Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

Süha Berk KUKUK; Zeynep Hilal KİLİMCİ

doi:https://doi.org/10.22399/ijcesen.950045

Article Details

Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

Authors:

Süha Berk KUKUK | Zeynep Hilal KİLİMCİ

Publisher:

İskender AKKURT

Main Topics:

Applied physics

Journal:

International Journal of Computational and Experimental Science and Engineering 2021 Vol.7 Issue 2, pp.84-94

Document Type:

Article

Publication Date:

7/2021

Document Language:

English

Document ID (Org No.):

2000477006

Frequency:

Tri-annual

eISSN:

2149-9144

DOI:

https://doi.org/10.22399/ijcesen.950045

Abstract:

 

Forest fire detection is a very challenging problem in the field of object detection. Fire detection-based image analysis have advantages such as usage on wide open areas, the possibility for operator to visually confirm presence, intensity and the size of the hazards, lower cost for installation and further exploitation. To overcome the problem of fire detection in outdoors, deep learning and conventional machine learning based computer vision techniques are employed to determine the fire detection when indoor fire detection systems are not capable. In this work, we propose a comprehensive analysis of forest fire detection using conventional machine learning algorithms, object detection techniques, deep and hybrid deep learning models. Experiment results demonstrate that convolutional neural networks outperform other methods with 99.32% of accuracy result.

Article Details

Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

Authors: Süha Berk KUKUK | Zeynep Hilal KİLİMCİ

Publisher: İskender AKKURT

Main Topics: Applied physics

Journal: International Journal of Computational and Experimental Science and Engineering 2021 Vol.7 Issue 2, pp.84-94

Document Type: Article

Publication Date: 7/2021

Document Language: English

Document ID (Org No.): 2000477006

Frequency: Tri-annual

eISSN: 2149-9144

DOI: https://doi.org/10.22399/ijcesen.950045

Issues

Authors:

Süha Berk KUKUK | Zeynep Hilal KİLİMCİ

Publisher:

İskender AKKURT

Main Topics:

Applied physics

Journal:

International Journal of Computational and Experimental Science and Engineering 2021 Vol.7 Issue 2, pp.84-94

Document Type:

Article

Publication Date:

7/2021

Document Language:

English

Document ID (Org No.):

2000477006

Frequency:

Tri-annual

eISSN:

2149-9144

DOI:

https://doi.org/10.22399/ijcesen.950045