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ارائۀ راهکاری مکانمند به‌منظور بهبود مدیریت امداد و نجات پس از زلزله | ||
| مدیریت مخاطرات محیطی | ||
| مقاله 2، دوره 6، شماره 2، تیر 1398، صفحه 117-129 اصل مقاله (622.48 K) | ||
| نوع مقاله: پژوهشی کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jhsci.2019.283168.476 | ||
| نویسندگان | ||
| ناهید بهرامی1؛ میثم ارگانی* 2؛ محمدرضا جلوخانی نیارکی3؛ علیرضا وفایی نژاد4 | ||
| 1دانشجوی دکتری، سنجش از دور و سامانه اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران | ||
| 2استادیار، گروه سنجش از دور و سامانۀ اطلاعات جغرافیایی، دانشکدۀ جغرافیا، دانشگاه تهران، تهران، ایران. | ||
| 3استادیار، گروه سنجش از دور و سامانه اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران | ||
| 4دانشیار، دانشکدۀ مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران. | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| الگوریتم ازدحام ذرات؛ امداد و نجات؛ زلزله؛ مدیریت بحران؛ مکانمند | ||
| عنوان مقاله [English] | ||
| Providing a Spatial Approach in the Rescue and Relief Management after the Earthquake | ||
| نویسندگان [English] | ||
| Nahid Bahrami1؛ Meysam Argany2؛ Mohammadreza Jelokhani Neyaraki3؛ Alireza Vafaeinezhad4 | ||
| 1Ph.D. Candidate, RS & GIS, Geographic Factually, University of Tehran, Tehran, Iran | ||
| 2Assiatan Professor, Department of RS & GIS, Geographic Faculty, University of Tehran, Tehran, Iran | ||
| 3Assiatan Professor, Department of RS & GIS, Geographic Faculty, University of Tehran, Tehran, Iran | ||
| 4Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran | ||
| چکیده [English] | ||
| Every 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 complexes. 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 Used model Cost calculated for the entire operational team 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. | ||
| کلیدواژه‌ها [English] | ||
| Relief and Rescue, Spatial, Crisis Management, earthquake, Particle Swarm Algorithm | ||
| مراجع | ||
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