Open Access
Research Article
Wildfire detection using modified particle swarm optimization algorithm
Suchita Arorasuchita.arora1@poornima.edu.inDepartment of Computer Science and Engineering Amity University Rajasthan ; Department of Computer Science and Engineering Poornima UniversityDepartment of Computer Science and Engineering Poornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , *Sunil KumarCorresponding authorsunilkumar.hqr@gov.inDepartment of Computer Science and Engineering Amity University RajasthanDefence Research and Development Organization Near Metcalfe HouseJaipur, New Delhi, 110054, IndiaView full profile → , Sandeep Kumarsandeepkumar.hqr@gov.inDepartment of AI and Data Science Engineering CHRIST (Deemed to be University)Defence Research and Development Organization Near Metcalfe HouseNew Delhi, New Delhi, 110054, India0009-0005-7107-3914View full profile → , Bhupesh Kumar Singhbksingh@jpr.amity.eduDepartment of Computer Science and Engineering Amity University RajasthanJaipur, Rajasthan, 303002, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2025
- Published Online:
- 01 May 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2303
- Pages:
- 2129–2138
Abstract
Wildfires claim many lives yearly and cause huge environmental and economic damage. Some of the worst wildfire disasters can be found in world history. Reducing CO2 emissions is critical to prevent irreversible change in the world climate in the era of global warming. Wildfire is the primary factor that leads to high CO2 emissions, promoting global warming. Early detection and prevention of wildfires are essential tasks of computer vision. An automated wildfire classification system can help prevent wildfires at an early stage. Many methods are used to identify wildfires and automate categorizing wildfires based on images. This paper offers a new path to wildfire classification based on a wildfire image dataset. This paper introduces a modified particle swarm optimization algorithm for classifying forest fire images using the image dimensions and characteristics that identify forest fires. The proposed approach uses a dataset to identify forest fires. Whether it is classified as a fire is based on the images of fire and smoke in the air. The proposed approach used an image dataset to validate and test the algorithm and outperform it in terms of precision, recall, F-score, and accuracy.
Keywords
Subject Classifications
Primary 68T01Secondary 68Q32
References
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