An algorithm of the wildfire classification by its acoustic emission spectrum using Wireless Sensor Networks

Khamukhin, Alexander A.; Demin, Anton Yurievich; Sonkin, Dmitry Mikhailovich; Bertoldo, Silvano; Perona, Giovanni Emilio; Kretova, V.

doi:10.1088/1742-6596/803/1/012067

Cited by 12 publications

(9 citation statements)

References 4 publications

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“…An example of this approach is phenology, which deals with the study of periodic plant and animal life cycles, and how some events are related to seasonal and climate variations [ 1 ] and, therefore, to global warming. A further example is provided by environmental monitoring operations, such as the use of the wildfire acoustic emission spectrum as the indicator of the type of forest fire [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks

Sendra

Gómez-Bellido

Carrasco

et al. 2018

Sensors

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The analysis and classification of the sounds produced by certain animal species, notably anurans, have revealed these amphibians to be a potentially strong indicator of temperature fluctuations and therefore of the existence of climate change. Environmental monitoring systems using Wireless Sensor Networks are therefore of interest to obtain indicators of global warming. For the automatic classification of the sounds recorded on such systems, the proper representation of the sound spectrum is essential since it contains the information required for cataloguing anuran calls. The present paper focuses on this process of feature extraction by exploring three alternatives: the standardized MPEG-7, the Filter Bank Energy (FBE), and the Mel Frequency Cepstral Coefficients (MFCC). Moreover, various values for every option in the extraction of spectrum features have been considered. Throughout the paper, it is shown that representing the frame spectrum with pure FBE offers slightly worse results than using the MPEG-7 features. This performance can easily be increased, however, by rescaling the FBE in a double dimension: vertically, by taking the logarithm of the energies; and, horizontally, by applying mel scaling in the filter banks. On the other hand, representing the spectrum in the cepstral domain, as in MFCC, has shown additional marginal improvements in classification performance.

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Section: Introductionmentioning

confidence: 99%

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks

Sendra

Gómez-Bellido

Carrasco

et al. 2018

Sensors

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“…Various approaches have been examined, ranging from spotting towers, to satellite or drone-based methods (Chowdary et al, 2018). A method employing acoustic detection on wireless sensor networks (WSNs) was outlined by Khamukhin et al (2017). As they point out, methods based on the detection of threshold exceedance in air temperature, relative humidity or smoke content (Molina-Pico et al, 2016; Varankumar et al, 2017) are only successful when the fire front is quite close to the sensors, which does not support very early warning of fire spread.…”

Section: Introductionmentioning

confidence: 99%

Acoustic methods in physical geography: Applications and future development

Dunkerley

2022

Progress in Physical Geography: Earth and Environment

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Sound is produced by many geomorphic and hydrological processes, such as rockfalls and landslides, ocean waves, fluvial flood flows and collisions among moving bedload clasts. In these and other areas of study, acoustic methods have found useful application to detect and quantify the operation of important landscape processes. In some, such as the recording of river discharge, the occurrence of rare events such as exfoliation or the presence and movement of dust devils (willy-willies), the use of acoustic methods is still in a relatively early stage of development and testing. The use of acoustic methods in the recording of rainfall occurrence and intensity is also developing and has the capacity to yield data with higher temporal resolution than can be achieved using conventional rain gauges. Novel acoustic methods include the analysis of the sound recorded by security cameras, which potentially form a vast network of observing stations. The frequencies of sound generated by land-surface processes include audible sound, ultrasound and infrasound at frequencies below the human hearing range. All appear to provide opportunities for further development of useful research tools and methodologies.

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“…Thus, wildfires cannot always be detected promptly. Additionally, the speed of wildfire spreading is high, especially in the face of a strong wind, which leads the wildfire being out of controlled and causes casualties [7,8]. In addition, wild regions are generally far away from infrastructure, so it is impractical to render a video device due to the energy requirement.…”

Section: Introductionmentioning

confidence: 99%

Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things

Zhang

Gao

Lin

et al. 2019

Sensors

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Wildfire is a sudden and hazardous natural disaster. Currently, many schemes based on optical spectrum analysis have been proposed to detect wildfire, but obstacles in forest areas can decrease the efficiency of spectral monitoring, resulting in a wildfire detection system not being able to monitor the occurrence of wildfire promptly. In this paper, we propose a novel wildfire detection system using sound spectrum analysis based on the Internet of Things (IoT), which utilizes a wireless acoustic detection system to probe wildfire and distinguish the difference in the sound between the crown and the surface fire. We also designed a new power supply unit: tree-energy device, which utilizes the biological energy of the living trees to generate electricity. We implemented sound spectrum analysis on the data collected by sound sensors and then combined our classification algorithms. The results describe that the sound frequency of the crown fire is about 0–400 Hz, while the sound frequency of the surface fire ranges from 0 to 15,000 Hz. However, the accuracy of the classification method is affected by some factors, such as the distribution of sensors, the loss of energy in sound transmission, and the delay of data transmission. In the simulation experiments, the recognition rate of the method can reach about 70%.

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An algorithm of the wildfire classification by its acoustic emission spectrum using Wireless Sensor Networks

Cited by 12 publications

References 4 publications

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks

Acoustic methods in physical geography: Applications and future development

Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things

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