Table 3.

The selected reviewed papers that applied classification algorithm for forest fire detection.

Authors	Year	Method	Architecture	Accuracy	Application	Augmentation	Type of Data
Priya et al. [66]	2019	CNN	Inception V3	Accuracy - 98%	Classification	No	Satellite Image
Arteaga et al. [2]	2020	CNN	ResNet + VGG	Accuracy - 99.5%	Classification	Yes	Image
Benzekri et al. [71]	2020	RNN, LSTM and GRU	RNN, LSTM, GRU	Accuracy - 99.89%	Classification	No	Image
de Almeida et al. [43]	2020	CNN	ResNet18	Specificity - 99%	Classification	Yes	Image
Rahul et al. [70]	2020	CNN	ResNet-50, VGG-16, DenseNet-121	Accuracy - 92.27%	Classification	Yes	Image
Ban et al. [69]	2020	CNN	CNN	Accuracy - 83.53%	Classification	No	Satellite Image
Jiang et al. [73]	2021	CNN	BP NN, GA, SVM, GA-BP	Accuracy - 95%	Classification	No	Image
Ghosh and Kumar [83]	2022	CNN	RNN	Accuracy - 99.62%	Classification	Yes	Image
Kang et al. [82]	2022	CNN	CNN & RF	Accuracy - 98%	Classification	Yes	Satellite Image
Khan and Khan [78]	2022	CNN	FFireNet, MobileNetV2	Accuracy - 98.42%	Classification	Yes	Image
Mashraqi et al. [90]	2022	DIFFDC-MDL	hybrid LSTM-RNN, MobileNet V2	Accuracy - 99.38%.	Classification	No	Image
Mohammad et al. [81]	2022	CNN	Resnet 50, GoogleNet, CNN-9 Layers, MobileNet, InceptionV3, AlexNet	Accuracy - 99.42%	Classification	Yes	Image
Mohammed [39]	2022	CNN	Inception-ResNet	Accuracy - 99.09%	Classification	Yes	Image
Gayathri et al. [80]	2022	CNN	CNN	Accuracy - 96%	Classification	No	Image
Alice et al. [92]	2023	Deep Transfer Learning	Quasi Recurrent Neural Network (QRNN), ResNet50 and optimize parameter used Atom Search Optimizer	Accuracy - 97.33%	Classification	No	Image