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@article{195531,
        author = {Umamaheswararao Mogili},
        title = {Deep Learning-Based Early Forest Fire Detection System for Environmental Disaster Management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {550-555},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195531},
        abstract = {One of the most hazardous natural disasters, forest fires may seriously harm wildlife, the environment, and even human lives. The likelihood of wildfires has increased dramatically due to climate change and rising global temperatures. The goal of this project is to create a forest fire detection system that automatically detects fire in photos by utilising deep learning techniques, notably Convolutional Neural Networks (CNNs). This system's objective is to give an early warning approach by precisely and swiftly identifying fire through the analysis of visual data. The algorithm learns to differentiate between fire and non-fire scenes after being trained on a dataset that includes pictures of both types of scenes. If put into practice, the system's ability to detect fire in real-time through image recognition techniques may assist stop wildfires from spreading. This solution is lightweight and simple to install because it was completed entirely with software and no additional hardware components. The results demonstrate that deep learning can be quite effective in wildfire identification, even though this is still a simple prototype. This strategy might be incorporated into a bigger system to more efficiently monitor and manage forest fires with additional accuracy and performance enhancements.},
        keywords = {Convolutional Neural Networks (CNN), Image Classification, TensorFlow, Keras, Real-Time Fire Alert System.},
        month = {April},
        }