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

Abstract

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.

Event Classification and Intensity Discrimination for Forest Fire Inference With IoT

“…The fuzzy sets, as described above, are defined by the rule of thirty [27]. The proposed fuzzy rules also consider the significance of smoke as a prime indicator for forest fire detection.…”

“…Considering the rough terrain, four parameters were considered in this work: Temperature, Smoke (C O 2 , N O 2 ), Wind Speed and Humidity which have been widely used for early fire detection in outdoor environment. Although, several other factors such as wind direction, fuel content in leaves etc, contribute to the detection of forest fires, but, as suggested in the literature [27], the chosen parameters have been found sufficient to accurately predict and classify the forest fires. All the other reported parameters were considered as noise and were discarded.…”

“…Thus, for all the values of temperature, relative humidity, wind speed and smoke density, the proposed model delivers an observation that can be placed under 'low', 'medium', 'high', or 'extreme'. To have an accurate estimation of the parameters, the rule of thirty [27], which considers that a value of temperature and wind speed above the value of 30 • C and 30 km/ h, respectively, combined together with a value of relative humidity value below 30 % would result in a favourable environmental condition for a forest fire.…”

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

“…IoT also gives birth to new technologies such as WSN (Wireless Sensor Network) [12] and Drone [13]. Even though IoT can be used as a single node, such as in health care applications to monitor patient heart rate, blood pressure, body temperature, and others [14], IoT can also be used as a network to expand the IoT application coverage such as forest fire drone monitoring [15] and others [16]. This IoT is called a Wireless Sensor Network.…”

GIS application in analysis of threat of forest fires and landslides in the Svrljiski Timok basin (Serbia)

“…Простор Републике Србије угрожен је различитим природним непогодама: земљотресима, клизиштима, снежним лавинама, бујичним поплавама, сушама и шумским пожарима. Последице настале деловањем природних катастрофа могу бити значајне по становништво у људском и материјалом погледу (Blaikie et al, 2014;Novkovic et al, 2021;Sirin & Medvedeva, 2022;Singh et al, 2022). Појава и дистрибуција шумских пожара повезана је са сезонским и временским корелацијама истих (Kim et al, 2021).…”