انجمن مخاطره شناسی ایرانمدیریت مخاطرات محیطی2423-415X6220190622Locating the Establishment of Rescue and Rescue Centers in Nahavand City Using the FAHP Fuzzy-AHP Modelمکانیابی مراکز امداد و نجات در شهرستان نهاوند با استفاده از مدل فازی- ای اچ پی FAHP971157277210.22059/jhsci.2019.280717.467FAهادینیریاستادیار گروه ژئومورفولوژی، دانشکدۀ منابع طبیعی، دانشگاه کردستان.0000-0003-4907-8274محمدرضاکرمیاستادیار دانشگاه پیام نورمحمودسوریدانشجوی کارشناسی ارشد مخاطرات محیطی دانشگاه کردستان.Journal Article20190506Introduction <br />The hazard is an inevitable part of life [1]. Population growth and urbanization have made it necessary to reduce the negative effects of human and social damage on natural and human hazards. Geologically, the county of Nahavand has the coastline near the Gamasiab river basin, which turned Nahavand into a seismic zone [8]. Garon and Nahavand faults were accompanied by a massive earthquake movement, between 1997 and 1963. This county is the most densely populated area in Hamadan province [14]. In Nahavand, most villages are located in the Nahavand Plain due to good agricultural conditions and on the active fault zone of the region. This county has 170 hectares of Worn-out urban texture [18]. Considering above mentioned cases, the need to locate relief and rescue centers in order to maximize coverage of rescue operations in the minimum possible time, far from urban services, is highlighted. Increasing the number of service centers is not due to better service, but the optimal distribution of these centers has a significant impact on the type of operation [20]. However, the location of relief centers for crisis management is one of the few cases that have been dealt with in practice. This research has been carried out with the aim of planning and locating spaces to provide relief and rescue centers in Nahavand earthquake. <br />Research methods and analysis <br />The county of Nahavand has an area of 1570 km<sup>2</sup> in the province of Hamedan. The city is located between 48 ° 20 ' and 48 ° 25' longitude, and 34° 0' and 34 ° 5 ' latitude, between high mountains of Zagros and Alvand. <br />In the present study, two clusters with twelve priority criteria were used to locate these centers. Flood plain, landslides, distances from faults, distance from landslide, drainage networks, soil and geology within the framework of environmental cluster criteria and demographic characteristics (urban centers, rural density, rural population), communication networks and land use as the most important human cluster criteria. These criteria are associated with a high population density that meaning more casualties and more damage during an earthquake [11]. Therefore, these centers should be closer to areas with high population density. After preparing the above criteria in the vectorized database, polygon, line, and point data were converted to raster maps in order to be capable to process. <br />Then the spatial data domain was identified. Using spatial data inputs its matrix is formed to descriptive data based on the suitable and not-suitable of criteria and the fuzzy threshold of each layer. Before the fuzzy layers overlap with the final map, the weights of all layers were obtained to construct the weight matrix of the layers using the Solver AHP Fuzzy software. The weights were standardized by the Chang method and the fuzzy pair comparison matrix was determined. After multiplying the normalized weights in the layers, the layers were overlaying through the Fuzzy sum method. During the analyzation, based on functions and thresholds of fuzzy logic, the data output will map the zones of appropriate locations for the establishment of rescue and rescue centers in the Nahavand earthquake. Finally, the correlation matrix was used to validate the layers and the relationship between 12 criteria of this study by SUM layer. <br />Result and Discuss <br />At first, urban centers map, rural centers, communication networks, and village population, and maps of distances from faults, river, and floodplains rasterized according to the incompatibility of the greatest distances. Then, after evaluating the vector layers, geology, soil, land use, and in field, the coding of the layers was determined from 1 to 6 in terms of the coefficient of the significance of each layer. This means that 1 is very important and 6 is the most important. Before layers fuzzification, the fuzzy pair comparison table will be specified. For this purpose, weights of each layer were obtained. So, in the Fuzzy AHP solver software, all weights were defined for the construction of the matrix. The weight of each criterion was then standardized according to Fuzzy Chang, from normalized weights, because the total weights are 1. In the next step, the obtained weight for each criterion in the process of analyzing the AHP network is multiplied in its fuzzy layer and the final value of the areas is obtained .In order to select the appropriate method for maps overlay, the best method that presented the result was the Fuzzy sum operator. The output of the final map was made on five levels. The results showed that 12 percent of the city's area, equivalent to 186 square kilometers, is very suitable for natural and human areas. These centers were seen along the longitudinal plane. The correlation matrix was used for validation. The first layer is the SUM layer and its relationship to the other layer is identified. There is a relationship between all layers that can show the correctness of the model. <br />Conclusion <br />The fault map of Hamedan province shows that the concentration of active faults in Nahavand is more than other parts of Hamedan province. Therefore, the present study addresses the issue of locating suitable spaces for the establishment of relief and rescue centers during natural hazards. Then, by providing the criteria and indicators that were the result of scientific and targeted research, the location of these centers was carried out with the view that such environments should be safe and away from high-risk environments with maximum coverage. Locating relief and rescue centers using GIS, and AHP hierarchical evaluation of the combination and the Fuzzy model by overlapping the fuzzy layers based on the suitable and Not-suitable layers. The findings showed that near 12 percent of the area has potential to create these centers, which spread across the plain of Nahavand, and cover the entire plain. Such a specialty for crisis management in this area, which has a great potential for seismicity, can be of great importance, because, in the event of an earthquake, it is possible to quickly provide services to earthquakes. زندگی انسان همواره با مخاطرات ناشی از بلایای طبیعی همراه بوده است. استقرار فلات ایران روی پهنۀ پرحادثۀ کرۀ زمین، کمربند زلزلۀ آلپ – هیمالیا، شرایطی را به وجود آورده که وقوع انواع مخاطرات طبیعی را اجتنابناپذیر کرده است. شهرستان نهاوند در یکی از حساسترین نقاط این فلات قرار گرفته، بهطوری که ﻭﺿﻌﻴﺖ ﻣﻮﺭﻓﻮﻟﻮﮊﻳﻚ شهرستان نهاوند ﻭ ﻗﺮﺍﺭﮔﻴﺮﻱ ﺁﻥ در یکی ﺍﺯ ﻧﻘﺎﻁ زلزلهخیز، ﺑﻪ ﺭﻭﻳﺎﺭﻭﻳﻲ ﻣﺪﺍﻭﻡ ﺍﻳﻦ شهرستان ﺑﺎ ﭘﺪﻳﺪۀ ﻃﺒﻴﻌﻲ ﺯﻟﺰﻟﻪ منجر ﺷﺪﻩ ﺍﺳﺖ؛ از اینرو برای ﺩﺳﺘﻴﺎﺑﻲ ﻋﻤﻠﻲ ﺑﻪ روشها ﻭ ﺭﺍهکارهایی ﻣﻨﺴﺠﻢ بهمنظور مدیریت بحران ﻭ ﺑﻪ ﺣـﺪﺍﻗﻞ ﺭﺳـﺎﻧﺪﻥ ﺍﺑﻌﺎﺩ فاجعهآمیز ﭼﻨﻴﻦ ﺭﺧﺪﺍﺩﻱ، مطالعه در این زمینه ضرورت دارد. یکی از مهمترین اقدامات قبل از مدیریت بحران که مستلزم مطالعۀ دقیق است، انتخاب بهینۀ مراکز امداد و نجات برای دسترسی و پوشش حداکثری مناطق آسیبدیده در کمترین زمان بهدلیل اهمیت نجات جان افراد در معرض خطر است. پژوهش پیش رو بر آن است که به مکانیابی بهینۀ مراکز امداد و نجات در زلزلۀ احتمالی نهاوند بپردازد تا در صورت وقوع حادثه، امدادرسانی در کمترین زمان میسر شود. بدین منظور مکانیابی این مراکز با توجه به معیارهایی چون راه ارتباطی، تمرکز جمعیتی، تراکم روستایی، فاصله از گسل و ... با بهره جستن از سیستم اطلاعات جغرافیایی GIS و مدل FAHP انجام گرفته است. نتیجۀ این تحقیق نشان داد که مکانهای مناسبی برای این مراکز بهدلیل شرایط مساعد طبیعی در منطقه وجود دارد که این ویژگی امتیازی بالقوه را برای مدیریت بحران در این شهرستان فراهم آورده است که باید از آن بهره جست. در نهایت سه محدوده در منطقه بهعنوان اولویتهای ایجاد مراکز امداد و نجات پیشنهاد شد.https://jhsci.ut.ac.ir/article_72772_f751bb5bced9eb1d839879ee2807bb28.pdfانجمن مخاطره شناسی ایرانمدیریت مخاطرات محیطی2423-415X6220190622Providing a Spatial Approach in the Rescue and Relief Management after the Earthquakeارائۀ راهکاری مکانمند بهمنظور بهبود مدیریت امداد و نجات پس از زلزله1171297298610.22059/jhsci.2019.283168.476FAناهیدبهرامیدانشجوی دکتری، سنجش از دور و سامانه اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایرانمیثمارگانیاستادیار، گروه سنجش از دور و سامانۀ اطلاعات جغرافیایی، دانشکدۀ جغرافیا، دانشگاه تهران، تهران، ایران.0000-0001-6577-4443محمدرضاجلوخانی نیارکیاستادیار، گروه سنجش از دور و سامانه اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران0000-0001-7448-7833علیرضاوفایی نژاددانشیار، دانشکدۀ مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران.Journal Article20190609Every year, many human beings suffer from an earthquake as a near-unpredictable natural disaster and its devastating human and financial losses. Management of such crises is related both before and after the crisis. Relief and rescue is only a stage in the occurrence of disasters can be studied in advance of the crisis to provide a solution to improve the performance of relief and rescue teams during the crisis. In this study, using a spatial information system and particle swarm algorithm and simulating a presumptive earthquake, a solution is suggested for optimal management of relief and rescue teams in earthquake. In this method, an earthquake, and 32 relief workers of four operational teams in 148 housing complexes simulated to study area in Tehran. Rescuers, with the help of particle swarm algorithm in a spatial information system, were allocated relief and rescue activities, in less time, would provide relief and rescue more efficient than the empirical mode. The use of this method to optimize simulations, as well as to implement the scientific and practical structure of relief and rescue teams and activities, will be a new way to improve the quality of relief and rescue after the earthquake. The results of the proposed method of this research showed Performance improvements of about twofold.
Introduction
One of the issues that most of the world's major cities face is the issue of natural disasters. The nature of the overwhelming majority of natural disasters and the need for quick and correct decision-making and implementation of operations has created knowledge of "crisis management". This knowledge refers to the set of activities that occur before, during and after the occurrence of disaster, in order to reduce the probable vulnerability caused by the occurrence of these events [5]. It is necessary to carry out all the affairs and actions necessary to achieve the goals outlined in the above definition, which requires the assumption of operational roles by operational teams [3]. Given the importance of relief and rescue at the time of natural disasters to save lives and property, the proper allocation of aid workers to activities is necessary.
In order to improve the relief and rescue operation, firstly, activities were carried out at the time of the earthquake, and comprehensive information were obtained on the post-earthquake relief and rescue mode. In order to allocate people, using optimization methods, considering the conditions of this research, is effective in improving the efficiency and effectiveness of post-earthquake relief. Hence, due to the nonlinear relations of this study, and in light of previous research, the particle swarm optimization algorithm was chosen as a suitable method for solving this problem. Moreover, also the use of a spatial information system for modeling, displaying, and updating of force information, activities, and conditions of earthquake area is suitable for optimal forces management [1].
Theoretical Foundations
Relief & Rescue
Review the tasks of the rescuers
This section examines the responsibilities of rescue workers in the earthquake crisis and important points in the earthquake relief process. Some search and rescue actors include four components of locating, evaluating, fixing, and transferring [19]. First, the location and release of individuals and the medical assessment and, if necessary, the use of primary care, emergency treatment (stabilization) and transfer to treatment centers are carried out [26]. The rescue team should have a precise program to carry out rescue operations for those in detention.
Search and Rescue Operations Management
To ensure the success of search and rescue operations in urban areas; it must be done very carefully. The relief and rescue program can be divided into five stages, respectively [26]:
Primary Identification - Data Collection (Preliminary Assessment)
Quickly assess the area (Technical Inspection)
Surface Search and Rescue in the Damaged Area (Primary Rescue)
Search and rescue by technical means (Secondary Rescue)
The systematic removal of debris (Final Collapse Lifting)
On the other hand, seven steps in search and rescue operations are assumed to be considered by the savior’s people [9]:
Data collection: One of the first steps to be taken is to assess and assess the situation.
Evaluation of Damage: By looking at different angles to the buildings.
Identifying resources and accessing them: including access to facilities, equipment, and personnel.
Priority: Includes emergency diagnosis and safety assurance for the continuation of search and rescue operations. Sometimes a building should be marked in such a way that no other person enters it and waits for other forces or more facilities.
Designing a Rescue Plan: In this section, it becomes clear who and with whom the conditions will enter the building.
Guidance for search and rescue operations: Search for people under the rubble remains and caught
Evaluate progress: The situation must always be checked to assess the progress of the rescue program and to prevent any damage to the relief forces
Particle Swarm Optimization (PSO)
The first attempt by Kennedy and Eberhart, simulating the social behavior of birds in 1995, presented the particle group optimization method. The components of a group follow a simple behavior. In this way, each member of the group imitates the success of their other neighbors. The purpose of such algorithms is to move members of the group to the search space and to accumulate at an optimal point (such as the source of food).
Methodology
To achieve relief & rescue optimal management, close interaction is being necessary [25]. The results of this study showed that parameters such as the duration of survival under the rubble, the duration, the distance between people and the location of activities, the speed of people when moving to the goal of the relief worker is very important in fulfilling the task. With the studies and studies, finally, the relation one was designed, which is a continuous nonlinear relationship. According to the studies, the method of optimizing the congestion of particle capabilities solves these functions, and this method allocates individuals to activities in this research is optimized:
(1)
In the above relationship, all parameters must follow a unit or reputation [24], “Max Injured” the most injured number among the wounded of each residential building, “Area Assigned” is the area [20], which the same activity is located inside it. “Spacing” the relief distance to the operating area and the “search time” and “search speed” are respectively the duration of the work and the speed of the relief worker. If a rescuer will be sent to a region that is estimated to be several people under debris, the duration of activities will be multiplied by the number of submarines. Moreover, to achieve the final cost of an activity that requires several people, it must be summed of the costs from each who performs that activity.
Result and Discussion
The cased study is a part of the central region of Tehran. The relief and rescue activities of the earthquake crisis include Searching, Light Collapse Lifting, Heavy Collapse Lifting, Primary Helping, Securing, Pointing, Securing Pilot, Air update in the rubble, reconstruction of the network of roads [6, 19]. In this research, 32 reliefworkers of four operational teams [22], and at the beginning of the operation, they are deployed at the nearest crisis management center to the study area. Figure 1 shows the first study area and the initial position of the relief workers in the study area.
Figure 2: Study area and the first location of rescuers
The following shows building and human damages data showing the initial phase of earthquake simulation, which includes 22 out of 148 damaged sites, and the descriptive information of relief workers in a hypothetical earthquake, in which 14 relief workers out of 32 relief workers, as well as the third, are shown their activities:
Fig. 2. building and human damages
Fig. 3. Descriptive information of relief workers
Regarding the parameters stated in the method of implementation (i.e.; the descriptive information of the rescuers, the activities and initial damages of the earthquake), the proposed algorithm of this research, is evaluated and calculated by using relations discussed for all the rescuers in all the housing complex<strong>es.</strong> And eventually, the allocated of relief workers to the activities was obtained. An example of the optimal mode of relief and rescue teams is showing in the figure below.
Fig. 4. Optimization of the Relief & Rescue Team
In the study area of the image above, the “Rescuers 34” relate to relief workers assigned to Light Collapse Lifting activities; “Rescuers32”, relief workers, and Pointing; “Rescuers31”, rescuers assigned to Searching activities. As well as “Rescuers33” for rescue workers who are engaged in Securing Pilot and relief workers “Rescuers 37”, engaged in Primary Helping activities. The allocation of people is carried out according to the priority, and the residential areas that have more damage are in the priority of the relief effort.
In evaluating the efficiency of the proposed algorithm, the positive effect of the initial population selection method shown in the results obtained from the implementation of the proposed algorithm. Finally, a 2.2 fold improvement in the results obtained from the state that was not used by this algorithm. In the table below, the calculation of the cost function in the two modes of implementation of the proposed algorithm and its non-implementation is set, which represents the calculating the cost of the allocation in the two situations for the entire operational team.
Table 1. Comparison of the results of the proposed algorithm and its validation
<strong>Used model</strong>
<strong>Cost calculated for the entire operational team</strong>
Without using the proposed algorithm
0.564
Using the proposed algorithm
0.252
Conclusion
Due to the facts that the problem is considered to be grouped of the subject of this research, the effectiveness of each person's activity on the other people's activities, and the group and the category of operations, as well as the structure of the particle swarm algorithm, which allows for more repetition in less time, the proposed algorithm of this study is identified as an appropriate solution to the post-earthquake relief and rescue problem.
The structure of the particle swarm algorithm is continuous; because of the discrete structure of the present, it is implemented discretely by applying changes to the structure of this algorithm. As previously stated, the context of individuals, their specializations, the activities, and the damaged sites have the same priorities as those that were implemented in the algorithm.
Using the proposed algorithm of this research and applying the changes expressed in it, in order to optimize and implement the scientific and practical structure of relief and rescue operation activities and teams, is a novel and effective way to improve the quality of relief and rescue after it will be an earthquake. Finally, as shown in Table 1 in the findings, the proposed algorithm implementation in this study improved the 2.2% of the results from the allocation of relief workers to a state that was not used by the proposed algorithm of this study.
For future researches, the optimization methods such as simulated annealing, ant colony, genetics, and game theory are suggested.زلزله، بحران و بلای طبیعی تقریباً پیشبینیناپذیری است که همهساله انسانهای زیادی به خسارات جبرانناپذیر جانی و مالی آن دچار میشوند. مدیریت اینگونه بحرانها هم به پیش از وقوع آنها مربوط میشود و هم به پس از آن. امداد و نجات، جزء مراحلی در وقوع حوادث است که میتوان با مطالعه و بررسی آن پیش از وقوع به راهکاری برای بهبود عملکرد گروههای امداد و نجات در هنگام بحران رسید. در این پژوهش با بهرهگیری از سیستم اطلاعات مکانی و الگوریتم ازدحام ذرات و شبیهسازی زلزلهای فرضی، راهکاری برای مدیریت بهینه گروههای امداد و نجات در وقوع زلزله پیشنهاد میشود. در این روش، زلزلهای فرضی در تهران شبیهسازی شد و 32 امدادرسان در قالب چهار گروه عملیاتی در 148 مجتمع مسکونی در محدودۀ تحقیق به انجام وظیفه پرداختند. امدادگران با استفاده از الگوریتم ازدحام ذرات در یک سیستم اطلاعات مکانی، بهگونهای به فعالیتهای امداد و نجات اختصاص یافتند که در زمان کمتری، امداد و نجات بیشتر و نیز کارامدتر نسبت به حالت تجربی و سنتی انجام دهند. استفاده از این الگوریتم برای بهینهسازی شبیهسازیها و نیز اجرای ساختار علمی و عملی فعالیتها و گروههای عملیاتی امداد و نجات، راهکاری نوین برای بهبود کیفیت امداد و نجات پس از زلزله خواهد بود. نتایج اجرای الگوریتم پیشنهادی این پژوهش، بهبود حدود دوبرابری تخصیص صورتپذیرفته را نشان داد.https://jhsci.ut.ac.ir/article_72986_c427f916af5736ece7f2f94784af8bd6.pdfانجمن مخاطره شناسی ایرانمدیریت مخاطرات محیطی2423-415X6220190622Rapid Damage Mapping after an Earthquake using Sentinel-2 Images (Case Study: Sarpol-e Zahab)شناسایی سریع مناطق آسیبدیده پس از وقوع زلزله با استفاده از تصاویر ماهوارهای Sentinel-2 (مطالعۀ موردی: زلزلۀ سرپل ذهاب)1311487298310.22059/jhsci.2019.284544.487FAمیلادجانعلی پوراستادیار، پژوهشگاه هوافضا، وزارت علوم، تحقیقات و فناورینادیاعباس زاده طهرانیاستادیار، پژوهشگاه هوافضا، وزارت علوم، تحقیقات و فناوری0000-0001-9247-4868حکمت الهمحمد خانلوکارشناس ارشد ژئودزی، دانشگاه آزاد شاهرود، سمنان، شاهرودالههخصالیدانشجوی دکتری سنجش از دور، دانشگاه صنعتی خواجه نصیرالدین طوسیحمیدعنایتیکارشناس ارشد فتوگرامتری، دانشگاه صنعتی خواجه نصیرالدین طوسیJournal Article20190531Rapid damage mapping after an earthquake in order to produce damage map is important for relief and rescue operations. Recently, the use of remote sensing images for producing damage maps is considered due to their synoptic view and low cost. In this research, a rapid damage mapping approach according to change detection is proposed, which is applied to the 2018 Sarepol-e Zahab earthquake. In order to assess results, outcomes of the change detection were evaluated using ground truth, which show high accuracy in detecting change areas. On the other hand, our damage map was evaluated using damage map produced by the European Space Agency (ESA), which outcomes depict our proposed method can detect damage areas by an overall accuracy of 84 %. Using the proposed method, damage map of the Sarepol-e Zahab was generated less than 30 minutes.
Introduction
Remote sensing is a useful science and technology for different applications, especially disaster management. Remote sensing can be used to produce building damage maps after the earthquake. Recently, researchers used remote sensing data for producing building damage maps [1-4]. However, the used approaches are based on training samples. Preparing training samples is a time consuming task. For this reason, scientists would like to develop rapid damage mapping. Tiede et al. proposed a method to map damage areas of the Haiti earthquake using a shadow analysis approach. The proposed approach can produce damage areas after 12 hours [5]. The main goal of this paper is to develop a rapid damage mapping approach based on pre- and post-event images in Sarpol-e Zahab. The developed method benefits from decision making approaches to make a rapid map.
Methodology
The proposed method is done in four steps according to Figure 1. In the first step, some essential pre-processing tasks including georeferencing and radiometric correction are performed. In the second step, difference image is produced and some textural features are extracted from it. In the third step, change and unchanged areas are identified using three change detection approaches. Finally, TOPSIS decision making approach is employed to make a damage map.
Fig. 1. Workflow of the proposed method
Results
Since the proposed method is based on change detection, we applied it to two data sets. Results of change detection over two case studies present in Figure 2. According to validation results, the proposed approach can detect changed and unchanged areas with about 95 % accuracy.
Nearest neaghbour of Region 1
Nearest neaghbour of Region 2
Spectral angle mapper of Region 1
Spectral angle mapper of Region 2
Maximum likelihoo of Region 1
Maximum likelihoo of Region 2
Fig. 2. Results of change detection approaches over two study areas
Using pre- and post-event Sentinel-2 images and our proposed approach, damage map of Sarpol-e Zahab was produced. Figure 3 shows pre- and post-event Sentinel-2 images and damage map of the study area.
Fig. 3. Pre- and post-event Sentinel-2 images and damage map of the study area
The accuracy of our damage detection approach is assessed using damage map produced by European space agency (ESA). Table 1 depicts the confusion matrix regarding the accuracy of our proposed method. Based on this table, the overall accuracy of our proposed approach is about 70 %.
Table 1. the confusion matrix of our proposed approach
<strong>Overall acc. (%)</strong>
<strong>User acc. (%)</strong>
<strong>Producer acc. (%)</strong>
<strong>Damaged</strong>
<strong>Undamged</strong>
<strong> </strong>
68.26
43.85
68.84
18468
14442
Undamaged
85.77
680.6
39355
6527
Damaged
<strong> </strong>
Conclusion
In this paper, a rapid damage mapping approach is proposed to detect damage areas from Sarpol-e Zahab earthquake. The proposed method is based on change detection and unsupervised. From the perspective of change detection, our proposed approach is robust. To assess the capability of the proposed method, it was applied in Sarpol-e Zahab earthquake. Using pre- and post-event Sentinel-2 images, the proposed approach can detect damaged areas with an accuracy of 80 %.شناسایی سریع مناطق آسیبدیده پس از وقوع زلزله بهمنظور تولید نقشۀ آسیب، اهمیت زیادی در زمینۀ امداد و نجات دارد. در چند سال گذشته استفاده از تصاویر ماهوارهای برای تولید نقشۀ تخریب بهدلیل سرعت زیاد، پوشش وسیع از منطقه و هزینۀ اندک بسیار مورد توجه محققان قرار گرفته است. در این پژوهش، یک روش شناسایی سریع مناطق آسیبدیده مبتنی بر روشهای شناسایی تغییرات ارائه خواهد شد که دربارۀ زلزلۀ سال 1396 سرپل ذهاب اجرا میشود. بهمنظور اعتبارسنجی این روش، ابتدا نتایج روش شناسایی تغییرات ارزیابی شد که خروجیها نشاندهندۀ صحت زیاد روش در شناسایی مناطق تغییریافتهاند. از طرف دیگر، نتایج روش شناسایی آسیب که در زلزلۀ سرپل ذهاب اجرا شده با نقشههای آسیب تولیدشده توسط سازمان فضایی اروپا اعتبارسنجی شد که نتایج حاکی از صحت 84 درصدی در شناسایی مناطق آسیبدیده است. با بهکارگیری روش پیشنهادی، نقشۀ آسیب برای شهر سرپل ذهاب بسیار سریع و در مدت کمتر از سی دقیقه تولید شد.https://jhsci.ut.ac.ir/article_72983_a818ba265a8668f79e1df45a0ee24961.pdfانجمن مخاطره شناسی ایرانمدیریت مخاطرات محیطی2423-415X6220190622Flood Hazards Susceptibility Map and its Occurrence Probability using Shannon Entropy Model (Case Study: Firoozabad River Basin)ارزیابی عوامل مؤثر بر مخاطرات سیلاب و تهیۀ نقشۀ حساسیت و احتمال وقوع آن با استفاده از مدل آنتروپی شانون (مطالعۀ موردی: حوضۀ آبخیز رودخانۀ فیروزآباد)1491677298110.22059/jhsci.2019.279717.462FAمحمد ابراهیمعفیفیاستادیار گروه جغرافیای طبیعی (ژئومورفولوژی) دانشگاه آزاد اسلامی واحد لارستان ایرانJournal Article20190422Introduction
Among natural hazards, flood is very important. One of the main ways to reduce the risks of floods is to provide flood sensitivity maps. . Between 1988 and 1997, around 390000 people were killed by natural disasters in the world, 58% were flood victims, 26% were affected by a storm and another 16% by the earthquake. The total livelihoods in these 10 years was about 700 billion $. That is 33, 29, and 28 percent were respectively from storm, earthquake, and flood. In this regard, a worrying trend is the increasing trend of mortality and flood damage in the world in recent decades. Increasing population and assets in flood plains, and the changes in hydrosystems and the destructive effects of human activities have been a major cause of this trend. In this research, Shannon entropy model has been used to provide flood sensitivity maps.
Research and analysis
Firoozabad is a city with an area of 11917 km southwest of Fars province. The city of Firoozabad is located at 28 degrees and 50 minutes north latitude, 52 degrees and 34 minutes east, with an altitude of 1325 meters from sea level. The Firoozabad basin is one of the sub-basins of the Mordan watershed. The Mard watershed with an area of 4,785 and 400 hectares, extending to the territory of three provinces of Fars, Bushehr and Hormozgan. The area of this basin is 3165 meters high from Khorramkouh in southern Zagros to the Persian Gulf coast. For the study of flood, 34 flood waters were first selected in the Firoozabad basin, and then these 34 points were classified into two groups. With 22 points, 65 percent of the points for training and modeling, and 12 points, 35 percent of the locations that were not used in modeling were used for validation. First, a map of the status of the floods was developed. Then, 10 factors, slope, tilt, lithology, land use, NDVI, SPI, TWI, altitudes, rainfall and distances from the river, were selected as flood factors in Firoozabad basin.
Discussion
Prioritizing the effective factors in the occurrence of flood by Shannon entropy index showed that NDVI layers (2.03), rainfall (2.00), distance from river (1.89), SPI (385.1), altitudes (0.999), gradient with weight (0.932), lithology (0.478), TWI (0.379), and land use (0.280), respectively (0.184) have the highest lowest impact on flood events. NDVI is the most important factor in the occurrence of flood in terms of Shannon entropy. In order to prepare a flood susceptibility map, 10 factors were prepared. After final weight of each factor by Shannon Entropy model and its multiplication in the classes, the weight maps were combined and the final flood sensitivity map was prepared. Then, the final map was classified into 5 classes of very low, moderate, high and very sensitive sensitivity. According to the central part of the basin, the map is more susceptible to flood sensitivity. To provide a distance map from the river, using the multiring buffer command was used in ArcGis software, and classified into five classes. To provide the effective factor of elevation classes, a digital elevation model was used in ArcGis software, and classified into five classes. Two important factors (SPI) and (TWI) are known as important factors in connection with water. TWI is the amount of flow accumulation in each location in the catchment area and flow trend downstream by gravity. To ensure the accuracy and validation of the maps prepared in this study, a regional attribute index or ROC curve was calculated from the area after the field survey. The basis of the results of the ROC curve for the Firoozabad Basin, for predicting the surface area under the curve with 35% of the validation data is 92.42%, and for the success rate with 65% of the education data, is equal to 93.53%. Therefore, Shannon entropy model has acceptable accuracy in preparing a flood susceptibility map in Firoozabad basin.
Conclusion
Shannon entropy model has acceptable accuracy in preparing flood susceptibility map in Firoozabad basin. Entropy is one of the management approaches used to deal with irregularities, instability of turmoil and uncertainties in a system. According to the final map, the sensitivity of the flood around the Firoozabad River is susceptible to a high flood event. Therefore, the construction of residential lands and agricultural lands and gardens around the river should be avoided. According to the results of the study, a natural disaster sensitivity map such as flood is important to manage and plan the future. Preparation of flood susceptibility map and its occurrence probability using Shannon entropy model (case study of Firoozabad river basin) is necessary to prevent financial and financial losses to these sectors. در بین مخاطرات طبیعی، سیلاب اهمیت زیادی دارد. یکی از راههای اصلی کاهش خطرات ناشی از سیل، تهیۀ نقشههای حساسیت به وقوع سیل است. در این تحقیق از مدل آنتروپی شانون برای تهیۀ نقشههای حساسیت به وقوع سیل استفاده شد. ابتدا 34 نقطۀ سیلابی حوضۀ آبخیز فیروزآباد انتخاب شد و سپس این 34 نقطه به دو گروه طبقهبندی شدند که 22 نقطه، 65 درصد از موقعیت نقاط برای آموزش و مدلسازی و 12 نقطه، 35 درصد از موقعیت نقاط که در مدلسازی بهکار گرفته نشدهاند برای اعتبارسنجی استفاده شدهاند که ابتدا به تهیۀ نقشۀ موقعیت سیلها پرداخته شد و سپس 10 عامل شیب، جهت شیب، لیتولوژی، کاربری اراضی، NDVI، SPI، TWI، طبقات ارتفاعی، بارندگی و فاصله از رودخانه بهعنوان عوامل مؤثر در وقوع سیل در حوضۀ آبخیز فیروزآباد انتخاب شدند. اولویتبندی عوامل مؤثر در وقوع سیل توسط شاخص آنتروپی شانون نشان داد که لایههای NDVI (03/2)، بارندگی (00/2)، فاصله از رودخانه (89/1)،SPI (385/1)، طبقات ارتفاعی (999/0)، شیب با وزن (932/0)، لیتولوژی (478/0)، TWI (379/0) و کاربری اراضی (280/0) و جهت شیب (184/0) بهترتیب بیشترین تا کمترین تأثیر بر وقوع سیل را دارند. براساس نتایج منحنی ROC برای میزان پیشبینی مقدار سطح زیر منحنی با 35 درصد دادههای اعتبارسنجی برابر 42/91 درصد و برای میزان موفقیت با 65 درصد دادههای آموزش برابر 53/92 درصد است. پس مدل آنتروپی شانون دارای صحت قابل قبولی در تهیۀ نقشۀ حساسیت به وقوع سیل در حوضۀ آبخیز فیروزآباد است.https://jhsci.ut.ac.ir/article_72981_8d50714c15f54a67ba43bb64b520e41d.pdfانجمن مخاطره شناسی ایرانمدیریت مخاطرات محیطی2423-415X6220190622Assessment and Zoning of Flash Flood Risks based on MFFPI Model (Case Study: Islamabad Basin)ارزیابی و پهنهبندی خطر سیلابهای ناگهانی براساس مدل MFFPI (مطالعۀ موردی: حوضۀ اسلامآباد غرب)1691847298210.22059/jhsci.2019.283544.480FAمنصورپرویناستادیار گروه جغرافیا دانشگاه پیام نور، ایرانJournal Article20190615Introduction
An immediate flood is a rapid flood that is caused by heavy rainfall or sudden release of water over a short period of time while water flowing over the earth (Huang et al., 2013, p. 325). Great floods have the same consequences, hydrogeomorphological effects (Martin et al., 2012, 49), biological effects (Richter et al., 2000, 1469), and socioeconomic effects (Mirz, 2007, 233). This means creating high financial costs and reducing socioeconomic development (Moses et al., 2010, 112). Flash floods are causing severe material damage, human casualties and extreme erosion (Farhan and Ayid, 2017, 718). Although flooding is only heavy rainfall, the hydrological response varies depending on the slope physiognomic features, soil texture, ground cover, rock permeability, and range curvature (Tinco et al., 2018,593). In order to reduce the risk of flash flood, it is necessary to identify and classify areas with high flood hazard potential (Minya, 2013, p. 345). The Islamabad basin is located in the Zagros zone, and the lithologic and geological conditions have caused the geomorphologic features of the Jurassic mountains and the lagging mountains around the syncline plain of Islamabad. This basin is sometimes faced with the risk of flood and river floods due to the severe and short-lived rainfall (W., 1391, 79). Considering the flood history of the Islamabad basin, it is necessary to assess the potential risk of the flash flood. The purpose of this study is to evaluate and map the potential for a flash flood hazard using physiographic parameters.
Materials and methods
The Predicted Flood Potential Model (MFFPI) uses six parameters of physiognomy with specific coefficients to capture the potential flood hazard potential (Table 1). Each of the six parameters, classified according to the impact of the flash flood event, is classified into five classes, and each of these classes has a weight of 1 to 5 according to the role played by the amount of water accumulation. The weight of each parameter in each of the five sub-parameters is multiplied and the final score of each layer is calculated (Tinco et al., 2018,596). In this research, the MFFPI model is implemented in two stages. In the first step, each of the six parameters is used in the preparation of a potential risk map for the basin's sudden risk. In the second stage, based on Spearman correlation and linear regression, the effective parameters are selected and based on which the MFFPI model is implemented.
Findings
In the first step, the MFFPI's final potential mapping map shows that areas with very low potential and very low flood peculiarities are consistent with the basin's heights. These areas were not susceptible to water accumulation because of topographic conditions and the possibility of flooding was very low in these areas. Areas with a high potential hazard flash flood is within the reach of the Islamabad plain of the west and the bedrock of the main rivers of the Ravand River, and the topographic conditions of the area are the main cause of the high potential of the sudden flood in these areas. Using Spearman method, the correlation coefficient between six variables was used. The results of this method show that all parameters are correlated and the Sig value is zero. The results of linear regression show that four topographic slope variables, flow accumulation, domain curvature and land use are statistically significant and account for 96.6% of the potential flood index. The two-soil texture and rock permeability variables are not significant. In the second step, the final potential map of the MFFPI model is prepared using four parameters. The survey shows that areas with a low flood hazard potential correspond to the heights of the basin and are not prone to accumulation due to the slope. Areas with a high potential hazard has potential of a sudden flood within the reach of the Islamabad plain and on the main bed of the Ravand River.
Conclusion
The final map of the potential risk of flashflood event from the MFFPI model in the first phase indicates that 45% of the area of the Islamabad sub-basin has a high-risk potential. Also, 13% of the area in the basin has a medium risk potential and 42% of the area is located in a dangerous and very low area. The spatial distribution of hazard potential zones is subject to the topographic conditions of the basin and areas with a potentially hazardous location in mountainous areas that do not contribute to the accumulation and accumulation of water. Areas with a high-risk potential the flash flood in the plain areas of Islamabad and around the Ravand River bed is due to the fact that these areas are prone to accumulation and accumulation due to topographic conditions and the existence of a river. Four variables of topographic slope, flow accumulation, domain curvature, and land use explains 96.6% of the flood potential index and the potential flood event map in the second phase was based on them using the MFFPI model. A final flood potential map survey in the second stage showed that areas with a potential high risk of 34% of the basin area, areas with an average potential of 17%, and areas with a potential risk of 49% of the area of the Islamic Basin have been allocated. In the map of the potential for flash flood, in the second stage, the spatial distribution of hazard zones follows the topographic conditions and flow accumulation. The removal of soil texture parameters and rock permeability in the second stage reduced the area of high-risk areas and increased the area of low and middle areas. Finally, it can be acknowledged that the results of the MFFPI model at both levels indicate a high risk of flash flood in the area of the west Islamabad, and that the city and all its villages and its surface infrastructure are at risk of flash flood.سیلابهای ناگهانی پاسخ هیدرولوژیکی عوامل فیزیوگرافی حوضه به رخداد بارشهای سنگین است و وقوع آنها سبب خسارات مالی و جانی میشود. بهدلیل ماهیت پیچیدۀ سیلاب ناگهانی، پیشبینی آنها با دشواری زیادی روبهروست و ویژگیهای فیزیوگرافی حوضهها نقش مهمی در شکلگیری آنها دارد. مدل MFFPI با استفاده از شاخصهای فیزیوگرافی حوضهها به ارزیابی خطر سیلاب ناگهانی میپردازد. حوضۀ اسلامآباد غرب بهعلت شرایط فیزیوگرافی و رخداد بارشهای سنگین مستعد وقوع سیلاب ناگهانی است و در طی چند دهۀ گذشته رخداد سیلاب سبب خسارات گسترده در شهر اسلامآباد غرب و در سطح این حوضه شده است. هدف پژوهش حاضر، ارزیابی و پهنهبندی خطر سیلاب ناگهانی حوضۀ اسلامآباد غرب با استفاده از مدل MFFPI است. نتایج پژوهش نشان داد که نقشۀ نهایی پتانسیل خطر سیلاب ناگهانی در مرحلۀ اول نشان میدهد که 45 درصد مساحت حوضۀ اسلامآباد غرب در پهنه با پتانسیل خطر زیاد قرار دارد. توزیع فضایی پهنههای خطر پنجگانه از شرایط توپوگرافی و تجمع جریان حوضه تبعیت میکند. در ادامه با بهرهگیری از رگرسیون خطی پارامترهای موثرتر در تجمع و انباشت آب مشخص شده و چهار متغیر شیب توپوگرافی، تجمع جریان، انحنای دامنه و کاربری اراضی 6/96 درصد از شاخص پتانسیل سیلاب را توضیح میدهند. حذف پارامترهای کمتر مؤثر بافت خاک و نفوذپذیری سنگ در مرحلۀ دوم اجرای مدل، سبب کاهش مساحت نواحی با پتانسیل خطر زیاد سیل و افزایش مساحت نواحی با پتانسیل خطر کم و متوسط سیل شده و مناطق با پتانسیل خطر خیلی زیاد در ارتفاعات و حواشی آن که با واقعیت همخوانی نداشته حذف شد. نتایج مدل MFFPI در هر دو مرحله بیانگر پتانسیل زیاد خطر سیلاب ناگهانی در سطح دشت اسلامآباد غرب است و این شهر و همۀ روستاها و زیرساختهای سطح آن در معرض خطر سیلاب ناگهانی قرار دارند.https://jhsci.ut.ac.ir/article_72982_4c0cf2514d40a8198e3f23be70bf4303.pdfانجمن مخاطره شناسی ایرانمدیریت مخاطرات محیطی2423-415X6220190622Evaluation of Geomorphosites and Analysis of their Strengths and Weaknesses Using GAM and M-GAM Models (Case Study: Sardasht City)ارزیابی ژئومورفوسایتها و واکاوی قوتها و ضعفهای آنها با بهکارگیری مدلهای GAM و M- GAM مطالعۀ موردی: شهرستان سردشت1852047298510.22059/jhsci.2019.283578.481FAممندسالاریاستادیار گروه ژئومورفولوژی، دانشکدۀ منابع طبیعی، دانشگاه کردستانJournal Article20190615Introduction
Geomorphosites are Geomorphological sites the results of both internal and external dynamics over time. It has Scientific, Ecological, Cultural, Aesthetic and Economic values that play an immense role in understanding the Paleo-Geomorphological evolution of local areas. Considering the importance of the topics of Geotourism in the decades and especially in recent years, different countries have developed the national list of Geomorphosites. Accordingly, Geological and Geomorphologic diversity is a point for sustainable regional development in the form of Geologic Tourism and Geotourism. The Geotourism studies provide the basis for creating a management route at the Geomorphosites from beginning to end and ultimately leads to better development and promotion of Geomorphosites. The existence of these studies, focusing on assessing the position of Geomorphosites, especially the management approach, has led to a considerable progress toward the sustainable development.
Methodology
The main methodology of this research is based on Theoretical studies (Theoretical and Library), field studies in the path of recognition of Geomorphosites, as well as questioning with statistical analyzes in a quantitative-qualitative and analytical framework based on the GAM and M-GAM Model. In the research process, topographic maps of 1: 50000, Geological maps of 1: 100,000, along with Google Earth images, have also been used. The GAM method is a Geomorphosites assessment Model that was formally presented by Vujicic and colleagues in 2011. This method is based on Peralong (2005), Perera et al (2007), Zurus (2007) and Reynard et al. (2008) Models. The GAM methodology, including the main value (MV), includes scientific-educational value (VSE), aesthetic value (VSA) and protective value (VPr), each with four sub-criteria. Added value (AV) also includes functional value (VFn) with 6 sub-criteria and also touristic value (VFn) with 9 sub-criteria. The assessment of this method includes the main value with 12 sub-criteria and the added value with 15 sub-criteria, which is a total of 27 sub-criteria and formulated as follows:
GAM= MV(VSE+ VSA+ VPr)+ AV(VFn+VTr) (1)
Finally, based on the results of the evaluation, a matrix consisting of the main and the additional values is created. The two existing values are represented in two axes, X and Y, and are presented as a special chart of the GAM to display the Geomorphosites and their position.
Another Model is M-GAM. This Model is also a Geomorphosites evaluation method based on the GAM methodology. The structural difference and the improvements in it include the views of Tourists in the process of evaluating indicators. Visitors estimate the significance (Im) of each parameter from the 27 sub-criteria in the GAM method. Finally, the general formula for estimation in M-GAM is as follows:
M- GAM= Im(GAM)or Im(MV+AV)
Finally, based on the results of the evaluation, a matrix consisting of the main and the additional values is created And the Geomorphosites are interpreted.
Discussion
This research, aims to study the Geomorphosites analysis with the management approach. Accordingly, the research process involves two parts of the environmental analysis and effective parameters, the identification and selection of Geomorphosites, their quantitative assessment with a systemic view, and finally analysis of their position with the management approach. The study area has a high structural complexity, as a product of the Sanandaj-Sirjan zone activity, the prevailing trends in Azerbaijan as well as the Zagros Thrust. Therefore, the Geological conditions have led to the diversity of the form and process in the study area. Therefore, the above-mentioned conditions are the basis for potential in the Geomorpho- diversity and Geomorphosites regions of the study area. In the phase of its monitoring, fourteen Geosites were identified and selected from the study area. The fourteen Geosites were included (Shalmash waterfall, Razga waterfall, Bitoosh Karstic mass, Sarperdan karstic mass, Kachaka Cave, Homel mountains, Tarkhan mountains, Gravan Spring , Vazne Plain, Biwran region, Mirabad protected forest, Zab river (Kalwe plain), Rassoul Sheet Springs and Vazne river). In the next step, the quantitative evaluation phase was performed based on the opinion of 15 experts. The results of the assessment based on GAM Model showed that the Shalmash, Mirabad, Biwran and Gravan geosites occupy the highest position. The Kachaka cave geomorphosites, which have the lowest and the next rank, are the Sarperdan mass, Bitoosh and Razga waterfall. From the perspective of the GAM diagram, it was also observed that most of the Geomorphosites are located in the Z21, Z22 and Z32 zones. Based on the existing Matrix based on the experts' opinions, the range of obtaining the main value points is 5/5 to 9/75 and the added value is from 4 to 9/25.
Based on the M-GAM Model, the Geomorsites were also evaluated. The results showed that the three Geomorphosites of Shlomash, Gravan, and Biwran had the highest scoring value. The reason was also related to aesthetic values. The results of the Geomorphosite evaluation also determined the importance of the main values, and there is a need to pay more attention to benefit.
Conclusion
The final analysis showed that in high-grade Geomorphosites, scientfic-educational value and aesthetic value were very high but their protective value was low. For instance, in the highest Geomorphosite such as the Shalmash waterfall, the protective value was 2.5, while the Scientific and Aesthetic value was between 3.5 and 3/75. The results of this study showed that in the most of the Geomorphosite the functional value is high, But in lower Geomorphosites, the touristic value is less than the functional value. For example, in the lowest Geomorphosite, such as the Kachaka cave, the tourist rating is 1/25 to 1/5, while the functional rating is 2.75 to 3.
The analysis of the matrix and the special chart of GAM also showed that most of the Geomorphsites are located in Z21, Z22 and Z32 zones. Therefore, it indicates that the main values and related sub-values are higher than the added values which is debatable from a management perspective. Therefore, the study of Geomorphosites with a management approach in the route of sustainable regional development showed that due to the inadequate position of conservation value in Geomorphosites, Geoconservation strategy should be considered in them. The results of this study showed that some Geomorphosites are active and some are inactive, one of the most significant Geoconservation policies is to educate the local community on the path to Geomorphosites participation. Also, the results based on M-GAM showed that the Touristic value has a lower level than the Functional value, Therefore, one of the basic strategies for sustainable development of Sardasht city is the identification and advertising at the weak Geomorphosites, and also branding at the high level of Geomorphosites. Therefore, this process is valuable for the Sardasht city, which is a borderline and deprived area.<strong> </strong>در سالهای اخیر ژئوتوریسم جایگاه مهمی در مباحث مرتبط با توسعۀ پایدار پیدا کرده و بهعنوان یک محور بهویژه برای جوامع محلی کمترتوسعهیافته مطرح شده است. تجربههای موجود در این حوزه در جهان نشان میدهد که مدیریت مؤثر و واقعی در این مسیر بسیار اهمیت دارد. بر این اساس، شناسایی ژئومورفوسایتها و پایش واقعی آنها با رویکرد قوتها و ضعفها، از مسیرهای درست در این حوزه است. در این پژوهش این مسئله در شهرستان سردشت با روش GAM (برمبنای نظر کارشناسان) و نیز روش M-GAM (برمبنای نظر بازدیدکنندگان)، برای اولین بار در یک چارچوب سیستمی- مقایسهای بررسی شد. نتایج بررسی، شناسایی 14 ژئومورفوسایت شاخص بود که با نظر کارشناسی تعیین شد. واکاوی آنها با روش GAM، نشان داد که ژئومورفوسایتهای آبشار شلماش و جنگلی میرآباد بیشترین ارزش امتیازی و ژئومورفوسایت غار کهچهکه و تودۀ کارستیک سرپردان کمترین ارزش امتیازی را دارند. با اینحال، تحلیل جایگاه ژئومورفوسایتها براساس ماتریس GAM و نیز نگرش مدیریتی نشان داد که ارزش اصلی در سطح آنها نسبت به ارزش افزوده عیار بیشتری دارد، ولی بررسیهای دقیقتر نشان داد که در ارزش اصلی نیز عیار حفاظتی نسبت به عیار علمی و زیباییشناختی در سطح پایینتری قرار دارد. بررسی ژئومورفوسایتها با روش M-GAM نیز نشان داد که ژئومورفوسایتهای شلماش، گراوان، بیوران بالاترین جایگاه را دارند. دلیل اصلی آن نیز در ارتباط با پارامترهای زیباییشناختی و علمی است که بیشتر مدنظر بازدیدکنندگان است. تحلیل نمودار GAM نشان داد که ژئوسایتهای مطالعاتی اغلب در سه زون Z<sub>21</sub>، Z<sub>22</sub> و Z<sub>32</sub> قرار گرفتهاند و برمبنای نمودار M-GAM نیز اغلب در دو زون Z<sub>21</sub> و Z<sub>22</sub>قرار گرفتهاند. تفاوت زونها مربوط به اعمال ضریب Im توسط بازدیدکنندگان است که در نوع مدیریت آنها مهم است. از طرفی بررسیهای دقیقتر نشان داد که عیار توریستی نیز نسبت به عیار کارکردی در سطح ژئومورفوسایتهای با ارزش امتیازی کم، جایگاه مهمی ندارد. بنابراین وجود راهبرد ژئوکانزرویشن بسته به فعال یا غیرفعال بودن آنها و نیز ایجاد و توسعۀ زیرساختها بهترتیب در سطح ژئومورفوسایتهای کمتر فعال و فعال ضروری است. بررسی پارامترها در دو مدل موجود پژوهش نشان داد که افزایش سطح دسترسی از طریق توسعۀ راههای ارتباطی اصلی و نیز افزایش سطح امکانات رفاهی در سطح ژئومورفوسایتها و بهویژه توسعۀ اقامتگاههای بومگردشگری که زمینۀ اشتغال نیز هستند بسیار کاراست. نتایج پارامترها نشان داد که برندسازی نیز یکی از حوزههای مناسب است، ولی ضرورت جایگاه آموزش و آگاهیسازی جوامع محلی در پایداری ژئومورفوسایتها بسیار مهم است. با تمام این موارد میتوان گفت که حرکت درست در این مسیر در سطح شهرستان سردشت، زمینهساز بهبود فضای کسبوکار، افزایش اشتغال پایدار، تحقق راهبرد ژئوکانزرویشن، توسعۀ امنیت مرزی و نیز ایجاد جایگزین مناسب برای مفاهیم شغلی کاذب همچون کولبری و در نهایت توسعۀ پایدار است.<br /> https://jhsci.ut.ac.ir/article_72985_7f6eb6c7179a3ac30c6f14acdcf95ff4.pdf