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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

Ahmad, Khubab; Khan, Muhammad Shahbaz; Ahmed, Fawad; Driss, Maha; Boulila, Wadii; Alazeb, Abdulwahab; Alsulami, Mohammad; Alshehri, Mohammed S.; Ghadi, Yazeed Yasin; Ahmad, Jawad


Khubab Ahmad

Muhammad Shahbaz Khan

Fawad Ahmed

Maha Driss

Wadii Boulila

Abdulwahab Alazeb

Mohammad Alsulami

Mohammed S. Alshehri

Yazeed Yasin Ghadi


Background: Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset.
Results: The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with the given data.
Conclusion: The integration of the robust feature extraction of FireXnet with the transparency of explainable AI using SHAP enhances the model’s interpretability and allows for the identification of key characteristics triggering wildfire detections. Extensive experimentation reveals that in addition to being accurate, FireXnet has reduced computational complexity due to considerably fewer training and non-training parameters and has significantly fewer training and testing times.

Journal Article Type Article
Acceptance Date Aug 30, 2023
Online Publication Date Sep 20, 2023
Publication Date 2023
Deposit Date Oct 17, 2023
Publicly Available Date Oct 17, 2023
Print ISSN 1933-9747
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 19
Article Number 54
Keywords Wildfire, Fire detection, CNN, Transfer learning, Lightweight architecture
Public URL


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