In December 16 – 21, 2005 occurred a wild spire FOREST FIRE on about 150 hectares of Gilan and Mazandaran FORESTs. In regarding to study of these less precedence FIREs, data of frequency, area and time of FIREs and also their synchronized meteorological data were obtained. The following results are based upon the analysis of the weather pattern of the FIRE period by using Franciele method and synoptic-dynamic analysis in the study period: during the FIRE relative in the lee slopes of Alborz relative humidity decrease to %20 and temperature increase to 25oc and in the windward slopes have occurred precipitation and also cloudy conditions. For this reason it seems föhn event has been main factor of FIRE. Also in the period the calculated Franciele coefficient (k ) shows high dangerous stage of FIRE ability (0.3 < k). In the case of weather conditions, in these times, the surface maps shown a high pressure tongue that indicated by1020mbisobar and along from a high pressure in west of china with a 1050mb closed sell in its centre. the tongue covered internal area of Iran and southern slopes of Alborz Mountains. In the upper maps it has seen that a ridge with north – south or northwest - southeast axe located upon west-half and north-western of Iran so its upstream portion located on south and west of Alborz Mountains and downstream portion located on north of Iran. Sort of pattern makes possible to move the westerlies from Alborz mountain created fohn wind. Both zonal and meridional wind components are positive that’s mean westerlies moved from southwest direction and created a suitable condition for föhn phenomena.

This research was done to model the FIRE spread using Alexandridis model and cellular automata method in part of District Three of Neka Zalemroud FORESTs. The effective factors on FIRE spread were elevation, slope, wind velocity and direction, vegetation type and vegetation density, based on the mentioned model (Alexandridis model). To simulate of the FIRE spread and evaluate its accuracy, the required data of actual FIRE including the FIRE start point, FIRE area, wind velocity and direction in FIRE time was provided. All effective parameters for actual FIRE confine were provided and coded in GIS. Finally the FIRE spread model was implemented using programming of Alexandridis model in MATLAB, regarding to Moore neighborhood and loading of digital layers of all effective parameters for actual FIRE confine. Some cellular situations and FIRE transfer rules were considered to predict the FIRE spread based on cellular automata method. The dynamic exhibition of the FIRE spread was presented by selecting the FIRE start point and number of burned and unburned cells was determined. Results of the model output including FIRE spread direction and form was compared with the actual FIRE confine to evaluate the accuracy of the used model qualitatively. The total precision and Kappa index were used to evaluate the accuracy of the used model quantitatively. The total precision and Kappa index were 0.88 and 0.74 respectively that it can show the precision of prediction of the FIRE spread model and the cellular automata method in modeling of FIRE spread in current research.

FOREST FIRE causes devastating destruction of FOREST as an important part of human environment, releases damaging atmospheric emissions and can be a threat to public health. It is one of the instances of natural crises and how to cope with it is an example of crisis management. Identifying the influential factors in the occurrence and propagation of FOREST FIRE and allocating resources in a proper manner considering risk zone mapping and their relationship with geographical and vegetation aspects are crucial for FIRE prevention and preparedness policies. Therefore, analytical process hierarchy (APH), geographic information systems (GIS), and remote sensing (RS) methods were applied to prepare FIRE risk maps for 30000-hectare-wide protected area of Manesht and Ghalarang in Ilam province. Investigating high risk zones and geographical aspects of the region, several naturally occurring FIRE breaks were recognized and used to divide this area into 33 subdivisions in order to decide about proper action plans on FIRE times. Results showed that human-started FIREs were the major cause of FOREST FIRE in this region. Among the many factors considered, vegetation thickness had very dominant effects on FIRE propagation. The findings were used to give some instructions on FIRE crisis management in the protected area including the three phases of preliminary action or activity before occurrence (prevention scheme), action during on occurrence (collation scheme), and action after occurrence (renovation scheme).

In recent years, FOREST FIREs have increased drastically due to global warming. FOREST FIRE prediction is the best way to control the spread of FIRE. Therefore, several studies have focused on developing models that predict the behavior of FOREST FIREs. Predicting FIRE spread and its behavior is crucial to mitigate the adverse effects on weather conditions, environment, and human activities. Improving FOREST FIRE prediction using higher quality data can be expensive. In some cases, obtaining or even precise estimation of these data is difficult. On the other hand, using prediction models are more reasonable and feasible to increase prediction accuracy. In this paper, we introduced a novel Belief-Desire-Intention (BDI) agent-based model to predict the behavior of FOREST FIREs in the Mazandaran region in the north of Iran. This paper attempted to map the concepts of BDI agent architecture into generic GIS. A novel BDI-GIS model was then proposed in which an agent’ s belief, desire, and intention were defined based on spatial or non-spatial data and GIS functions. Therefore, an agent-based model was developed to determine the prediction of FOREST FIREs and implemented it on a real dataset. The experimental results showed that the proposed model could be successfully applied to the real-world scenarios with a Kappa Coefficient of more than 68. 2%.

Fighting FOREST FIREs to avoid their potential dangers as well as protect natural resources is a challenge for researchers. The goal of this research is to identify the features of FIRE and smoke from the unmanned aerial vehicle (UAV) visual images for classification, object detection, and image segmentation. Because FORESTs are highly complex and nonstructured environments, the use of the vision system is still having problems such as the analogues of flame characteristics to sunlight, plants, and animals, or the smoke blocking the images of the FIRE, which causes false alarms. The proposed method in this research is the use of convolutional neural networks (CNNs) as a deep learning method that can automatically extract or generate features in different layers. First, we collect data and increase them according to data augmentation methods, and then, the use of a 12-layer network for classification as well as transfer learning method for segmentation of images is proposed. The results show that the data augmentation method used due to resizing and processing the input images to the network to prevent the drastic reduction of the features in the original images and also the CNNs used can extract the FIRE and smoke features in the images well and finally detect and localize them.

Several factors play an important role in the occurrence of FIRE in FORESTs. So accurate prediction of the time and place of the FIRE is difficult, but using the GIS, it is possible to identify points of high FIRE risk. The purpose of this study was to investigate the possibility of preparing a FIRE hazard zonation map for Ilam district. Socioeconomic, climatic, topographic and vegetation factors were considered as suitable criteria for assessing the status of FIRE risk of these FORESTs and were evalutaed Using satellite imagery data, geographic information system and neural network system. According to the results, the variable of distance from cities with a weight of 100% is the most influential variable in creating FIRE and height variable with a weight of 1. 5% as the least important variable in the occurrence of FOREST FIRE in study area. Based on the results of the assessment of the accuracy of the FIRE hazard zonation map using the error matrix, it was found that the produced map with a general accuracy of 73. 73% is highly true. Also, based on Kappa agreement coefficient (0. 77) between the map and actual FIRE data, it can be concluded that the map prepared with the actual data was highly adapted.

Introduction: WildFIREs cause substantial losses of property and human lives in ecosystems in Iran and all around the world. Every year, about 6000 ha of FORESTs are affected by FIREs in Iran (Jahdi, Salis, Darvishsefat, Mostafavi, Alcasena, Etemad, Lozano, & Spano, 2015). These FIREs can occur naturally due to climate change or abnormally caused by human activities in the FORESTs. In fact, FIRE is one of the most important factors in disrupting the structure and dynamics of FOREST in natural conditions that produces a large amount of dead wood caused by the destruction of trees (Coban & Mehmet, 2010). As a result, there are significant costs to prevent FIRE every year in countries it takes place, and managing ecosystems is a way to reduce the effects of FIRE on war and maintain perspectives. To manage FIRE in FORESTs, understanding the behavior of the FIRE and the factors causing it in a prone environment and factors affecting its behavior are essential (Artes, Cencerrado, Cortes, & Margalef, 2013). One way to manage FIRE is FARSITE model. This model has become popular due to its steady structure and simplicity of popularity in the modeling of FIRE spreading. This model is considered as one of the main FIRE simulation systems used to describe the behavior and release of horrific FIREs and is a semi-experimental model (Shen, Prince, Gallacher, Fletcher, & Fletcher, 2017)...