year 13, Issue 2 (Summer 2023 2023)
E.E.R. 2023, 13(2): 235-253 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Salehi N, Dashti S, Atarroshan S, Nazarpour A, Jaafarzadeh N. Forest Risk Fire Zoning using an Integrated Method of Artificial Neural Network and Spatial Information System (Murray Study: Shimbar Protected Area). E.E.R. 2023; 13 (2) :235-253
URL: http://magazine.hormozgan.ac.ir/article-1-687-en.html
URL: http://magazine.hormozgan.ac.ir/article-1-687-en.html
Department of Environment, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. , solmazdashti@gmail.com
Abstract: (1225 Views)
1- Introduction
The diverse ecosystems in Iran with their own unique climate and wildlife have witnessed uncontrolled fires annually to the extent that in terms of forest fires, Iran ranks fourth among MENA countries (Naghipoor Borj, 2018). In 2017, a total of 252 incidents of wildfire occurred in Iran, with damage to 3,006 hectares; while in 2018, 187 wildfires occurred damaging 2,385 hectares (Sabzali et al., 2019). The Zagros forests cover an expanse of 6 million km2 in the West of Iran (approximately %4 of Iran’s total land mass) (Sadeghi et al., 2017) of which 33,920.2 km2 are located in the Khuzestan province (Alli Mahmoodi Sarab et al., 2013). The Shimbar Mountains are chiefly composed of limestone formations, and only a small area is composed of alluvial deposits. The average annual rainfall in the area is roughly 815 mm, and the average annual temperature is 20-26°C. The average evaporation rate for the area is 2,523 mm. (Sharifi et al., 2020). The vegetation cover of the Shimbar natural reserve is composed of two types of vegetation: the marshland vegetation cover which is chiefly Artemisia as the main vegetation cover, and the mountain vegetation cover which is a type of Iranian oak.
Due to the high security of the wildlife reserve, a variety of mammals thrive in this region such as the Iranian squirrel, martens, wolves, wild bear, and the mongoose. Birds such as quail, partridge, bee-eaters and woodpeckers are also native to the area (Dinarvand et al., 2018).
Shimbar region was decreed by national legislature to be among the four areas under the jurisdiction of the National Environmental Protection Agency (NEPA). In the early 1940s, the first attempts to apply a logic-based model to simulate fire hazard risks was carried out by Warren McCulloch and Walter Pitts, and this logic model is the basis of all present day artificial neural networks (Laurent Fast, 2016). In the present study, the underlying reason for the selecting of an artificial neural network was its capability in the creating a relationship between the input and output data for non-linear complex phenomena, its extensive application in the prediction of fire hazards, and its ability to create a model out of the relationship between the number of fires and the factors impelling such fires (Ouache et al., 2021 & Islami et al., 2020 & Polinova et al., 2019 & Jaafari Goldarq et al., 2013).
2- Methodology
Initially, the data related to forest fires that had occurred in the period spanning 2011 to 2018 were collected from the Andika regional environmental protection agency, and in the next stage, the ground reality maps of these points were prepared. The environmental protection agency had recorded 79 fires in the wildlife preserve; therefore, the data was used for the training and testing of the model used in this study. In order to prepare a map identifying potential fire hazard zones, the factors affecting the forest fires in the region were identified as chiefly being physiological features such as slope, aspect, and elevation. All topographic maps with a scale of 1:25,000 were obtained from the Andika regional environmental protection agency, and the National cartographic center. Features of the vegetation cover such as soil types, land classification types, vegetation cover, and land use were obtained from LANDSAT 8 images and the complementary data were obtained from the Natural resource's organization of Andika. Climate features such as relative humidity, wind speed, minimum temperature, maximum temperature, wind direction, amount of rainfall, and average temperature were accessed from the archives of the regional meteorological office. With due regards to the fact that the data values obtained were at various points, and in order to generate data for the whole region, the interpolation functions in ArcGIS were utilized. Anthropological features considered were far from residential areas, road accessibility, and distance from the river. The data for road accessibility were obtained from Google Earth layer maps and the data for residential areas and distance to rivers were provided by the Andika regional environmental protection agency and Natural resources offices. The Information layers for roads, rivers and residential areas used a vector format; therefore, by using Euclidean distance analysis, Raster-geomatics with the capability of spatial segregation for the required zones were developed in a way that the value allocated to each cell indicated the distance from the nearest road, the river or residential area. Once the features for each of the variables were identified, a spatial map was prepared in a GIS environment.
3- Results
The data used in this study encompass historical data of forest fires occurring from 2011 to 2018. By analyzing the maps created for various parameters such as the forest fire dispersion map, physiological features map, vegetation cover map, meteorological map, anthropological specification map, the results showed that the vegetation cover; the distance to the available bodies of water, and the type of lands are the main factors to be considered. The validation of the model was assessed by utilizing RMSE- ROC-AUC criteria in order to verify the accuracy of the obtained results for determining the extent of potential forest fires with actual events. It was observed that the method proposed in this research has an accuracy of 0.83 in predicting fluctuations along the trend which is relatively high. The data were then transferred to the Arc GIS software and the zoning map for determining the potential fire hazard areas based on the existing historical data was created and divided into five categories representing very low risk, low risk, moderate risk, high risk, and extremely high-risk fire hazard potential. The results/maps also indicated the percentage of fire hazard classifications based on the artificial neural network. It was observed that %31 of the areas are classified as extremely low risk; %28 are classified as low risk; %20 are defined as moderate risk; %11 are classified as high risk; and the remainder %10 are classified as extremely high risk zones.
4- Discussion & Conclusions
The artificial neural network, much like its counterpart in the human body, is independent of the distribution of the data, and is capable of adequately evaluating the problems besetting natural resources. Comparative studies carried out by Ngoc et al. (2018) and Sachdeva et al. (2018) showed that the artificial neural network can provide the best evaluation for determining the potential fire hazards in a region.
The current study has found that the recent forest fires have taken a severe toll on the Southern Zagros forests in the Shimbar wildlife preservation. By determining the effective factors influencing the risk of forest fires and incorporating these factors into an artificial neural network, a zoning map for the pinpointing of areas with the greatest risk of fire hazard has been created. The zones were classified into five categories, ranging from extremely low to extremely high-risk fire hazard regions. The validity of the model was evaluated as being 0.83 and the RMSE= 0.75, which in itself is indicative of the accuracy and efficiency of the artificial neural network method in developing a map for determining potential fire hazard areas. The obtained results were in parallel with the findings of a study conducted by Vidamanesh et al. (2018) who used artificial neural networks with a validity of 0.88 to determine the zones in the Mazandaran Forest and grasslands where there is a greater risk of forest fires. It also corroborated the findings of studies conducted by Anderson et al. (2021) on predicting the forest fires using artificial neural networks which had a validity of 0.81, and Elia et al. (2020) whose model had a validity of 0.82.
The graph for the importance of independent and dependent factors showed that in the artificial neural network the factors for scorched and unburnt areas (dependent variable), distance from the body of water, type of land classification, elevation and minimum temperature (independent variable) in sequence had the most important effect, and the direction of the slope had the least important impact on the risk of fire hazard in the region. Moreover, it was observed that %41 of the area under study was classified as being moderate risk, high risk and extremely high-risk zones. The results obtained paralleled those in the study of Eslamiee et al (2021) conducted on the forests in the Babolrud region in which by applying an artificial neuron network, it was seen that %35 of the region under study was classified as having a high to extremely high risk of fire hazard, and the most important variables affecting the occurrence of forest fire were defined as being temperature, rainfall, and distance from a rural center. The results of the current research were also in line with the results obtained by Zheng et al. (2019) who used an artificial neural network with an AUC = 0.86 to model the sensitivity of forested areas in China to fire hazards. The study stated that the most important variables affecting the occurrence of a forest fire are temperature, wind, rainfall and elevation.
Based on the results of the current study, the forested areas which are composed of oak, juniper, wild pistachio, almond, mesquites and tamarisk have a distance from the bodies of water and the vegetation cover in the region under study ranges from a thick canopy to an average canopy. The rural centers and roads among the forested area have a medium to low population density and are chiefly located in the northern and central areas of the region under study. The populated area is mostly located at elevations ranging from average to low asl, the region has a minimal slope, the temperature fluctuates from 50° C to 1.8° C (average temperature is 25.6° C) and low rainfall occurs.
Received: 2021/10/15 | Published: 2023/07/27
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
