Enhancing Forest Fire Detection: Integrated CNN And LSTM with Advanced Techniques

Sunny, Sebin Chembottikkal (2023) Enhancing Forest Fire Detection: Integrated CNN And LSTM with Advanced Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Forest fires pose serious threats to the environment and human safety, necessitating efficient detection methods. Traditional approaches relying on sensors or human observation are often expensive and error-prone. To address this, researchers are exploring advanced computer vision techniques, particularly leveraging convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This study compares the performance of CNNs and CNN Bi LSTM in detecting forest fires and smoke from aerial and ground views. This research used dataset of 12,631 images from Kaggle, of forest images for training and testing. The integrated approach of CNN Bi LSTM demonstrates superior performance, exhibiting over 90% accuracy in both aerial and ground views. The results indicate that the combined spatial and temporal analysis of CNN Bi LSTM surpasses the capabilities of CNN alone. This research contributes to the development of a robust forest fire detection model, emphasizing the effectiveness of combining spatial and temporal aspects in computer vision applications.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Nagahamulla, Harshani UNSPECIFIED
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science S Agriculture > SD Forestry Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence > Computer vision Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence > Computer vision
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	23 May 2025 12:49
Last Modified:	23 May 2025 12:49
URI:	https://norma.ncirl.ie/id/eprint/7622

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