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Using neural networks and a fuzzy inference system to evaluate the risk of wildfires and the pinpointing of firefighting stations in forests on the northern slopes of the Zagros Mountains, Iran (case study: Shimbar national wildlife preserve)

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Abstract

Predicting potential fire hazard zones in natural areas is one of the means of mitigating and managing fires. The current research focuses on the prioritizing of elements which contribute to the spread of fire and the special zoning of potentially dangerous areas in addition to the pinpointing of locations for the establishment of fire stations in forested areas in the Shimbar national reserve based on historical data spanning 2001 to 2018. The study utilizes elements (physiological, vegetation cover, meteorological, anthropological factors) contributing to wildfires as inputs into an artificial neural network and the development of a fuzzy inference system in order to produce fire zoning maps for the region under study. The map is divided into five sectors, i.e., minimum, low, moderate, high, and maximum risk of fire. The validation of the fire zoning map was evaluated at 0.83 and the RMSE error was 0.75. The results obtained show that 20% of the area under study is within the average risk category, 11% is within the high-risk category, and 10% is within the very high-risk category of a potential fire hazard. The most important variables were distance from a flowing source, i.e., river or stream, the land formation type, elevation, and the minimum temperature. The identification of suitable locations for firefighting stations was carried out by merging the fuzzy inference system model and Arc GIS, and the results obtained defined 16 possible locations. It was concluded that the application of hybrid models when dealing with the aforementioned variables is effective when seeking to determine locations for the establishment of firefighting stations and rural safety services; moreover, such hybrid models are highly efficacious for determining of fire hazard zones. It is proposed that hybrid models be applied on a large scale for the prevention, control, and management of fires throughout the country.

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Data availability

Data generated during the study are included in the manuscript. Data are available from authors on request.

Abbreviations

FIS:

Fuzzy inference system

ANN:

Artificial neural networks

GIS:

Geographic Information System

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Authors and Affiliations

  1. Department of Environment, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

    Nafieh Salehi, Soolmaz Dashti & Ahad Nazarpour

  2. Department of Environment, Persian Gulf Dust Research Center, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

    Sina Attar Roshan

  3. Department of Environment, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

    Neamatollah Jaafarzadeh

  4. Environmental Technologies Research Center, Ahvaz Jundishapu University of Medical Sciences, Ahvaz, Iran

    Neamatollah Jaafarzadeh

Authors
  1. Nafieh Salehi
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  2. Soolmaz Dashti
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  3. Sina Attar Roshan
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  4. Ahad Nazarpour
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  5. Neamatollah Jaafarzadeh
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Contributions

Nafiseh Salehi: conceptualization, methodology, validation, writing—original draft, and writing—review and editing. Soolmaz Dashti: investigation, project administration, data and curation Sina Attarroshan: investigation, writing—review and editing, and visualization. Ahad Nazarpour: investigation, software, and preparing figures. Neamatollah Jaafarzadeh: investigation and supervision.

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Correspondence to Soolmaz Dashti.

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Salehi, N., Dashti, S., Roshan, S.A. et al. Using neural networks and a fuzzy inference system to evaluate the risk of wildfires and the pinpointing of firefighting stations in forests on the northern slopes of the Zagros Mountains, Iran (case study: Shimbar national wildlife preserve). Environ Monit Assess 195, 294 (2023). https://doi.org/10.1007/s10661-022-10702-8

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