Unmanned Aerial Vehicle Assisted Forest Fire Detection Using Deep Convolutional Neural Network

Rahman, A. K. Z Rasel; Sakif, S. M. Nabil; Sikder, Niloy; Masud, Mehedi; Aljuaid, Hanan; Bairagi, Anupam Kumar

doi:10.32604/iasc.2023.030142

Cited by 18 publications

(8 citation statements)

References 34 publications

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“…A technique for recognising forest fires was proposed by Rahman et al [18] using a Convolutional Neural Network (CNN) architecture and freshly created fire detection dataset from another study. Their approach utilised separable convolution layers for rapid fire detection, making it suitable for real-time applications.…”

Section: Related Workmentioning

confidence: 99%

Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model

Aluri,

Devi,

Chinthakunta

et al. 2024

IJSSE

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A crucial natural resource that directly affects the ecology is forests. Forest fires have become a noteworthy problem recently as a result of both natural and man-made climatic changes. A smart city application that uses a forest fire discovery technology based on artificial intelligence is provided in order to prevent significant catastrophes. A major danger to the environment, animals, and human lives is posed by forest fires. The early detection and suppression of these fires is crucial. This work offers a thorough method for detecting forest fires using advanced deep learning (DL) algorithms. Preprocessing the forest fire dataset is the initial step in order to improve its relevance and quality. Then, to enable the model to capture the dynamic character of forest fire data, long short-term memory (LSTM) networks are used to extract useful feature from the dataset. In this work, weight optimisation in LSTM is performed using a Modified Firefly Algorithm (MFFA), which enhances the model's performance and convergence. The Variational Autoencoder Generative Adversarial Networks (VAEGAN) model is used to classify the retrieved features. Furthermore, every DL model's success depends heavily on hyperparameter optimisation. The hyperparameters of an VAEGAN model are tuned in this research using the Waterwheel Plant Optimisation Algorithm (WWPA), an optimisation technique inspired by nature. WPPA uses the idea of plant growth to properly tune the VAEGAN's parameters, assuring the network's peak fire detection performance. The outstanding accuracy (ACC) of 97.8%, precision (PR) of 97.7%, recall (RC) of 96.26%, F1-score (F1) of 97.3%, and specificity (SPEC) of 97.5% of the suggested model beats all other existing models, which is probably owing to its improved architecture and training techniques.

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Section: Related Workmentioning

confidence: 99%

Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model

Aluri,

Devi,

Chinthakunta

et al. 2024

IJSSE

View full text Add to dashboard Cite

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“…Strengthening FMU capacity will significantly improve forest fire control performance. In a recent study [44], Rahman et al proposed a method for detecting forest fires using a deep convolutional neural network based on the dataset for Forest Fire Detection from Mendeley Data. Experimental results of this research showed that the method could identify forest fires within images with an accuracy of 97.63% and a F1 score of 98%.…”

Section: Systematic Reviewmentioning

confidence: 99%

Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation

Khosravi,

Shafie,

Raihan

et al. 2023

Preprint

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Despite being considered as natural components of many ecosystems, forest fires pose significant threats to the environment and human health. In order to ensure public safety and effective fire suppression planning, it is necessary to develop reliable prediction models to mitigate forest fire danger. These models should account for specific environmental conditions. The advent of big data in recent years has opened new avenues for improving forest fire predictions. Machine learning techniques, surpassing traditional forecasting methods, have shown significant promise in this area. By applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to a real-world dataset, this paper explores the application of machine learning approaches to understand forest fire patterns and predict fire danger. We consider six distinct time stages and incorporate feature selection to refine our predictions. It is important to note that forest fire behavior models are not universally effective due to geographical variations in data. Nevertheless, advanced decision-making techniques are vital in forest fire management. Our research presents a systematic exploration of the topic, comparing various machine learning models, thereby providing a comprehensive baseline for future investigations in this crucial environmental arena.

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“…Unlike machine learning algorithms, deep learning automatically extorts and familiarizes complex feature representations [47]. "CNN-based models utilize frames from surveillance systems as input, and the predicted result is sent to an alert system [44].…”

Section: Introductionmentioning

confidence: 99%

A Synergistic Approach to Wildfire Prevention and Management using AI, Machine Learning, and 5G Technology in the United States

C. Stanley,

Alexander,

O. Austine

2024

Artificial Intelligence and Big Data

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In recent years, wildfires have emerged as a global environmental crisis, causing significant damage to ecosystems, and contributing to climate change. Wildfire management methods involve prevention, response, and recovery efforts. Despite advancements in detection methods, the increasing frequency of wildfires necessitates innovative solutions for early detection and efficient management. This study explores proactive approaches to detect and manage wildfires in the United States by leveraging Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology; Active monitoring and mapping with remote sensing and signaling leveraging on 5G technology; and Advanced response mechanisms to wildfire using drones and IOT devices. This study was based on secondary data collected from government databases and analyzed using descriptive statistics. In addition, past publications were reviewed through content analysis, and narrative synthesis was used to present the observations from various studies. The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively. This would save a lot of lives and prevent huge economic loss that is attributed to wildfire outbreaks and spread. Advanced technology can be used in several ways to help in the proactive detection and management of wildfires. This includes the development of the use of AI-enabled remote sensing and signaling devices and leveraging 5G technology for active monitoring and mapping of wildfires. In addition, super intelligent drones and IOT devices can be used for safer responses to wildfires. This forms the core of the recommendation to the fire Management Agencies and the government.

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Unmanned Aerial Vehicle Assisted Forest Fire Detection Using Deep Convolutional Neural Network

Cited by 18 publications

References 34 publications

Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model

Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model

Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation

A Synergistic Approach to Wildfire Prevention and Management using AI, Machine Learning, and 5G Technology in the United States

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