Forest fire spatial modelling using ordered weighted averaging multi-criteria evaluation

Abstract

Forest fires are a major environmental issue because they are increasing as a consequence of climate change and global warming. The present study was aimed to model forest fire hazard using the ordered weighted averaging (OWA) multi-criteria evaluation algorithm and to determine the role of human, climatic, and environmental factors in forest fire occurrence within the Golestan National Park (GNP), Iran. The database used for the present study was created according to daily classification of climate changes, environmental basic maps, and human-made influential forest fire factors. In the study area, the forest fires were registered using GPS. Expert opinions were applied through the analytic hierarchy process (AHP) to determine the importance of effective factors. Fuzzy membership functions were used to standardize the thematic layers. The fire risk maps were prepared using different OWA scenarios for man-made, climatic, and environment factors. The findings revealed that roads (weight = 0.288), rainfalls (weight = 0.288), and aspects (weight = 0.255) are the major factors that contribute to the occurrence of forest fire in the study area. The forest fire maps prepared from different scenarios were validated using the relative operating characteristic (ROC) curve. Values of forest fire maps acquired from scenarios of human, environment, climate factors and their combination were 0.87, 0.731, 0.773 and 0.819, respectively.

Using Machine Learning Techniques in Multi -Hazards Assessment of Golestan National Park, Iran

“…77 analyzed the synchronous patterns of wild re hazards in the Golestan province forests and pointed to the adverse effects of hot desert winds from Qarahqum on wild re events in the region. Results from the humidity variable also show that in areas with low humidity, and hence poor rangeland cover, the risk of wild re increases 78 .…”

“…Farmers burn agricultural waste to replant their land. In this case, the control of this wild re is out of reach and penetrates the national park 78 . However, at longer distances can be attributed to the interaction of other variables and the livelihoods of these villagers are in turn tied to the forests and pastures, and in this regard, the managerial and legal problems for managing the national park.…”

Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review

“…However, forest fires pose significant constraint to sustainable economic development in Africa; thus, it is necessary to implement fire susceptibility research to improve fire management strategies (Dlamini 2011;Suryabhagavan et al 2016). Moreover, with the recent increases in fire frequencies around the globe attributed to climate change it is urgent to conduct forest fire susceptibility research in fire prone regions, such as Latin America and Africa, to mitigate forest fires ecological, environmental and economic impacts (Faramarzi et al 2021;Milanović et al 2021;Sulova and Arsanjani 2021).…”

“…These alterations are attributed to climate change associated with drought and high temperature and anthropogenic activities such as changes in land use and demographics (Tuyen et al 2021;Dang et al 2021). The changes of fire regimes are impacting several domains which include the environment, atmosphere, society and economy (Faramarzi et al 2021;Bisquert et al 2013;Michael et al 2021;Hawbaker et al 2013). Therefore, it is crucial to understand and better predict forest fires in order to reduce these impacts and improve forest fire management (Manzo-Delgado et al 2005;Jain et al 2020).…”

Application of geospatial techniques and binary logistic regression model for analyzing driving factors of urban growth in Bahir Dar city, Ethiopia

“…The ROC tool evaluates/tests the relationships between the actual and the predicted changes. It computes the percentages of false positives, true-positives for a range of thresholds and relates them to each other [ 82 ]. The area under the curve (AUC), which can range from 0.5 to 1, is calculated using the ROC technique.…”