A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests

Wang, Maofa; Gao, Guangda; Huang, Hongliang; Heidari, Ali Asghar; Zhang, Qian; Chen, Huiling; Tang, Weiyu

doi:10.1109/access.2021.3122112

Cited by 2 publications

References 64 publications

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Drone network for early warning of forest fire and dynamic fire quenching plan generation

Manoj,

Valliyammai

2023

J Wireless Com Network

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Wildfires are one of the most frequent natural disasters which significantly harm the environment, society, and the economy. Transfer learning algorithms and modern machine learning tools can help in early forest fire prediction, detection, and dynamic fire quenching. A group of drones that has high-definition image processing and decision-making capabilities are used to detect the forest fires in the very early stage. The proposed system generates a fire quenching plan using particle swarm optimization technique and alerts the fire and rescue department for a quick action, thereby stop the forest fire at an early stage. Also, the drone network plays a major role to track the live status of forest fire and quenches the fire. ResNet, VGGNet, MobileNet, AlexNet, and GoogLeNet are used to detect the forest fire hazards. The experimental results prove that the proposed technique GoogLeNet-TL provides 96% accuracy and 97% F1 score in comparison with the state-of-the-art deep learning models.

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Drone network for early warning of forest fire and dynamic fire quenching plan generation

Manoj,

Valliyammai

2023

J Wireless Com Network

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Network of Drones for Early Warning of Forest Fire and Dynamic Fire Quenching Plan Generation

Manoj

Valliyammai

2023

Preprint

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Wildfires, one of the most frequent natural disasters, significantly harm society, the economy, and the environment. Transfer learning algorithm and modern machine learning tools can help in early forest fire prediction, detection, and dynamic fire quenching. Also, a dynamic fire quenching plan is prescribed which takes into account the live status of forest fire. A network of drones which has high definition image processing and decision making capabilities are used for detection of forest fire in the very early stage there by preventing the forest fire. In case fire breaks out, the proposed system generates a fire quenching plan and alerts the fire and rescue departments so that they do plan early and stop the forest fire as soon as possible. ResNet, VGGNet, Mobile Net, Alexi Net, GoogLeNet models enhanced with transfer learning are used to predict the forest fire events in India. It is found that the proposed technique GoogLeNet-TL, which uses transfer learning with GoogLeNet has resulted in 96% accuracy and 97.56% F1 score than other models considered for forest fire prediction in Indian subcontinent.

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A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests

Cited by 2 publications

References 64 publications

Drone network for early warning of forest fire and dynamic fire quenching plan generation

Drone network for early warning of forest fire and dynamic fire quenching plan generation

Network of Drones for Early Warning of Forest Fire and Dynamic Fire Quenching Plan Generation

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