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@article{187994,
        author = {D,Crystal JabaKani and S. Saudia},
        title = {Wildfire Prediction Using Modified Convolutional Neural Networks and Chromatic Variants},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {825-836},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187994},
        abstract = {Environmental disaster by wildfire is a serious issue of concern. This paper proposes an analysis on the performance of various CNN based Deep Learning models, AlexNet, Lenet, VGG16 and their modified versions in the color spaces: RGB, YUV and HSV for predicting wildfire. The dataset with ‘Fire’ and ‘NoFire’ images is taken from the DeepFire dataset. The images in the RGB color space are converted into YUV and HSV spaces before using the images for training the CNN models and their modified versions. The modified version of LeNet5 produces higher accuracy than all versions. YUV color space produces high accuracy with original LeNet5 and AlexNet models, RGB color space produces high accuracy with modified LeNet5 and AlexNet models. HSV space is better for the VGG16 models.},
        keywords = {Color Spaces, Convolution Neural Network (CNN), Deep Fire, Deep Learning, Forest Fire},
        month = {December},
        }