A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks

Zhu, Haoran; Gao, Demin; Zhang, Shuo

doi:10.32604/jiot.2019.05897

Cited by 22 publications

(14 citation statements)

References 4 publications

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“…The literature review of IoT-based sensor networks and WSN-based systems intended for the real-time forest fire risk prediction and fire outbreak detection showed the broad set of possible meteorological and other parameters used in these systems. The following is observed: The most commonly used set of parameters consists of: temperature (T), relative humidity (H), wind speed (W speed ), and rainfall (R), [ 17 , 18 , 57 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ]. For this set of parameters, the fuzzy AHP method is used [ 57 , 63 , 64 , 69 ] for the forest fire risk prediction, sometimes with additional parameters for human behavior and environment [ 64 ], while these same parameters were also combined with the concentrations of chemical gases (oxygen, carbon monoxide, and carbon dioxide) in order to detect forest fire outbreaks [ 57 ].…”

Section: General Description Of Adopted Iot-based System For the Forest Fire Monitoringmentioning

confidence: 99%

“…For this set of parameters, the fuzzy AHP method is used [ 57 , 63 , 64 , 69 ] for the forest fire risk prediction, sometimes with additional parameters for human behavior and environment [ 64 ], while these same parameters were also combined with the concentrations of chemical gases (oxygen, carbon monoxide, and carbon dioxide) in order to detect forest fire outbreaks [ 57 ]. Additionally, the same set of parameters is used as an input for artificial neural networks (ANN) [ 66 , 68 ] or linear regression model [ 70 ], or to calculate the fire weather index (FWI) [ 65 , 67 , 71 , 72 ] by using a pre-defined procedure, for the purpose of forest fire risk prediction. The narrowed set of parameters that include temperature and relative humidity is proposed for the forest fire risk prediction in combination with other sensors, such as vision sensors [ 73 , 74 ] with fusion realized using Dempster–Shafer evidential theory, smoke and light intensity sensors [ 75 , 76 ], or light intensity and carbon monoxide sensors [ 77 ] by using the fuzzy AHP method.…”

Section: General Description Of Adopted Iot-based System For the Forest Fire Monitoringmentioning

confidence: 99%

Section: General Description Of Adopted Iot-based System For the Forest Fire Monitoringmentioning

confidence: 99%

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GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

Novković

Marković

Lukic

et al. 2021

Sensors

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The territory of the Republic of Serbia is vulnerable to various natural disasters, among which forest fires stand out. In relation with climate changes, the number of forest fires in Serbia has been increasing from year to year. Protected natural areas are especially endangered by wildfires. For Nature Park Golija, as the second largest in Serbia, with an area of 75,183 ha, and with MaB Reserve Golija-Studenica on part of its territory (53,804 ha), more attention should be paid in terms of forest fire mitigation. GIS and multi-criteria decision analysis are indispensable when it comes to spatial analysis for the purpose of natural disaster risk management. Index-based and fuzzy AHP methods were used, together with TOPSIS method for forest fire susceptibility zonation. Very high and high forest fire susceptibility zone were recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP). The additional support for forest fire prevention is realized through an additional Internet of Thing (IoT)-based sensor network that enables the continuous collection of local meteorological and environmental data, which enables low-cost and reliable real-time fire risk assessment and detection and the improved long-term and short-term forest fire susceptibility assessment. Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and early warning systems in the Republic of Serbia, but are also important for relevant authorities at national, regional, and local level, which will be able to coordinate and intervene in a case of emergency events.

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Section: General Description Of Adopted Iot-based System For the Forest Fire Monitoringmentioning

confidence: 99%

Section: General Description Of Adopted Iot-based System For the Forest Fire Monitoringmentioning

confidence: 99%

Section: General Description Of Adopted Iot-based System For the Forest Fire Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

Novković

Marković

Lukic

et al. 2021

Sensors

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“…Nevertheless, it is usually affected by some elements, such as cloud thickness and water content over the fire scene; consequently, this technology is regularly used as an early warning method [28]. Besides, satellite data have a low spatial resolution, and the wildfire monitoring using thermal infrared channel data is easily interfered with by a sturdy reflection surface, high-temperature saturation, and other factors, which is not conducive to full tracking of a wildfire’s intensity spatial pattern [29].…”

Section: Related Workmentioning

confidence: 99%

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

Zhang

Gao

Lin

et al. 2019

Sensors

Self Cite

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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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“…The immune clonal selection (ICS) algorithm is a research hotpercal in the field of artificial immune system. This algorithm simulates immune mechanisms such as clonal selection, amplification of antibodies, high-frequency mutation [23][24][25], and receptor editing in the immune response process of the immune system [26]. Thus, it exhibits strong self-learning, self-organization, and self-adaptation ability [27], and is widely used in many engineering fields.…”

Section: Introductionmentioning

confidence: 99%

Zoning Permanent Basic Farmland Based on Artificial Immune System coupling with spatial constraints

2021

KSII TIIS

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The red line of Permanent Basic Farmland is the most important part in the "three-line" demarcation of China's national territorial development plan. The scientific and reasonable delineation of the red line is a major strategic measure being taken by China to improve its ability to safeguard the practical interests of farmers and guarantee national food security. The delineation of Permanent Basic Farmland zoning (DPBFZ) is essentially a multi-objective optimization problem. However, the traditional method of demarcation does not take into account the synergistic development goals of conservation of cultivated land utilization, ecological conservation, or urban expansion. Therefore, this research introduces the idea of artificial immune optimization and proposes a multi-objective model of DPBFZ red line delineation based on a clone selection algorithm. This research proposes an objective functional system consisting of these three sub-objectives: optimal quality of cropland, spatially concentrated distribution, and stability of cropland. It also takes into consideration constraints such as the red line of ecological protection, topography, and space for major

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A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks

Cited by 22 publications

References 4 publications

GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

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

Zoning Permanent Basic Farmland Based on Artificial Immune System coupling with spatial constraints

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