Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

Kukuk, Süha Berk; Kilimci, Zeynep Hilal

doi:10.22399/ijcesen.950045

Cited by 16 publications

(7 citation statements)

References 19 publications

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“…In this study, the improvement steps of the deep learning BP neural network forest fire identification algorithm are as follows ( Kukuk and Kilimci, 2021 ).…”

Section: Improvement Of Forest Fire Identification Algorithmmentioning

confidence: 99%

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

et al. 2022

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Forests are indispensable links in the ecological chain and important ecosystems in nature. The destruction of forests seriously influences the ecological environment of the Earth. Forest protection plays an important role in human sustainable development, and the most important aspect of forest protection is preventing forest fires. Fire affects the structure and dynamics of forests and also climate and geochemical cycles. Using various technologies to monitor the occurrence of forest fires, quickly finding the source of forest fires, and conducting early intervention are of great significance to reducing the damage caused by forest fires. An improved forest fire risk identification algorithm is established based on a deep learning algorithm to accurately identify forest fire risk in a complex natural environment. First, image enhancement and morphological preprocessing are performed on a forest fire risk image. Second, the suspected forest fire area is segmented. The color segmentation results are compared using the HAF and MCC methods, and the suspected forest fire area features are extracted. Finally, the forest fire risk image recognition processing is conducted. A forest fire risk dataset is constructed to compare different classification methods to predict the occurrence of forest fire risk to improve the backpropagation (BP) neural network forest fire identification algorithm. An improved machine learning algorithm is used to evaluate the classification accuracy. The results reveal that the algorithm changes the learning rate between 0.1 and 0.8, consistent with the cross-index verification of the 10x sampling algorithm. In the combined improved BP neural network and support vector machine (SVM) classifier, forest fire risk is recognized based on feature extraction and the BP network. In total, 1,450 images are used as the training set. The experimental results reveal that in image preprocessing, image enhancement technology using the frequency and spatial domain methods can enhance the useful information of the image and improve its clarity. In the image segmentation stage, MCC is used to evaluate the segmentationresults. The accuracy of this algorithm is high compared with other algorithms, up to 92.73%. Therefore, the improved forest fire risk identification algorithm can accurately identify forest fire risk in the natural environment and contribute to forest protection.

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“…In this study, the improvement steps of the deep learning BP neural network forest fire identification algorithm are as follows ( Kukuk and Kilimci, 2021 ).…”

Section: Improvement Of Forest Fire Identification Algorithmmentioning

confidence: 99%

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

et al. 2022

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“…The method to analyze and identify the spread of the haze in the air due to wildfire has been elaborated and discussed by [13][14][15] to determine how much the area is being polluted by poor air quality. A deep learning algorithm called LSTM implements modeling to plot the pattern of the fire hotspot data, but the forecasting in this work only covers a small area or designated specific zone.…”

Section: Introductionmentioning

confidence: 99%

Wildfire Hotspots Forecasting and Mapping for Environmental Monitoring Based on the Long Short-Term Memory Networks Deep Learning Algorithm

et al. 2023

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Global warming is raising the earth’s temperature, and resulting in increased forest fire events, especially in tropical regions with locations that are at high risk of wild and forest fires. Indonesia is a country in Southeast Asia that has experienced a severe number of wildfires, which have dangerous impacts on neighboring countries due to the emission of carbon and haze to the free air. The objective of this research is to map and plot the locations that consist of a significant number of fire hotspots and forecast the possible forest fire disasters in Indonesia based on the collected data of forest fires. The results of forecasting data are beneficial for the government and its policymakers to take preventive action and countermeasures regarding this wildfire issue. The Long Short-Term Memory (LSTM) algorithm, a deep learning method, was applied to analyze and then forecast the number of wildfire hotspots. The wildfire hotspot dataset from the year 2010 to 2022 is derived from the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS). The total number of collected observations is more than 700,000 wildfire data in Indonesia. The distribution of wildfire hotspots as shown in the results is concentrated mainly on two big islands, Kalimantan and Sumatra, Indonesia. The main issue is the peat type of land that is prone to spreading fire. Forecasting the number of hotspots for 2023 has achieved good results with an average error of 7%. Additionally, to prove that the proposed algorithm is working well, a simulation has been conducted using training data from 2018 to 2022 and testing data from 2021 to 2022. The forecasting result achieved a similar pattern of the number of fire hotspots compared to the available data in 2021 and 2022.

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“…Uncrewed aerial vehicles (UAVs) and video surveillance can obtain forest canopy image information using deep learning methods for fire monitoring. The complex features of the forest fire image, such as smoke and flame, are analyzed to build a forest fire monitoring/early warning model [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT

et al. 2023

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Monitoring and early warning technology for forest fires is crucial. An early warning/monitoring system for forest fires was constructed based on deep learning and the internet of things. Forest fire recognition was improved by combining the size, color, and shape characteristics of the flame, smoke, and area. Complex upper-layer fire-image features were extracted, improving the input conversion by building a forest fire risk prediction model based on an improved dynamic convolutional neural network. The proposed back propagation neural network fire (BPNNFire) algorithm calculated the image processing speed and delay rate, and data were preprocessed to remove noise. The model recognized forest fire images, and the classifier classified them to distinguish images with and without fire. Fire images were classified locally for feature extraction. Forest fire images were stored on a remote server. Existing algorithms were compared, and BPNNFire provided real-time accurate forest fire recognition at a low frame rate with 84.37% accuracy, indicating superior recognition. The maximum relative error between the measured and actual values for real-time online monitoring of forest environment indicators, such as air temperature and humidity, was 5.75%. The packet loss rate of the forest fire monitoring network was 5.99% at Longshan Forest Farm and 2.22% at Longyandong Forest Farm.

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Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

Cited by 16 publications

References 19 publications

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

Wildfire Hotspots Forecasting and Mapping for Environmental Monitoring Based on the Long Short-Term Memory Networks Deep Learning Algorithm

An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT

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