پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 154 |
| تعداد شماره‌ها | 5,823 |
| تعداد مقالات | 63,946 |
| تعداد مشاهده مقاله | 106,173,178 |
| تعداد دریافت فایل اصل مقاله | 83,080,226 |
رتبه‌بندی مناطق مسکونی شهری در برابر مخاطرات زمین‌لرزه با استفاده از روشهای آنتروپی شانون و تاپسیس (مطالعۀ موردی: شهر آمل) | ||
| مدیریت مخاطرات محیطی | ||
| مقاله 1، دوره 7، شماره 3، مهر 1399، صفحه 225-239 اصل مقاله (1.13 M) | ||
| نوع مقاله: پژوهشی کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jhsci.2020.306514.579 | ||
| نویسندگان | ||
| پرهام پهلوانی* 1؛ میعاد بادپا2 | ||
| 1استادیار دانشکدۀ مهندسی نقشه‌برداری و اطلاعات مکانی، پردیس دانشکده‌های فنی، دانشگاه تهران | ||
| 2دانشجوی دکتری اکتشاف معدن، دانشکدۀ مهندسی معدن، پردیس دانشکده‌های فنی، دانشگاه تهران | ||
| چکیده | ||
| در این پژوهش، از روشهای آنتروپی شانون و تاپسیس برای رتبه‌بندی مناطق مسکونی شهری در برابر خطر زمین‌لرزه استفاده شد. بدین منظور، به‌صورت موردی در 27 ناحیه از شهرستان آمل در استان مازندران، هشت متغیر شامل انرژی آزادشدۀ زمین‌لرزه‌های 20 سال اخیر برحسب تن TNT به‌ازای هر ناحیه، کیفیت ابنیه و ساختمان‌ها، تراکم مسکونی، تراکم ساختمانی، تراکم جمعیت، نفوذ‌پذیری شبکۀ معابر، فضای باز شهری و عمق آب زیرزمینی بررسی شد. این متغیرها با استفاده از روشهای آنتروپی شانون و مدل تصمیم‌گیری چندشاخصۀ تاپسیس، براساس نزدیکی به ایدئال آسیب‌پذیری رتبه‌بندی شدند. در محاسبۀ انرژی آزادشدۀ زمین‌لرزه‌های 20 سال اخیر از داده‌های دقیق مؤسسۀ ژئوفیزیک دانشگاه تهران استفاده شد، اما این مقادیر به‌دلیل کوتاه بودن دورۀ زمانی الزاماً گویای لرزه‌خیزی منطقه نبود و به همین علت وزن کمتری به این فاکتور اختصاص داده شد. در ادامه رتبه‌های به‌دست‌آمده از این تحلیل به نقشۀ شهر اضافه شده و ناحیه‌های شهری به بخش‌های دارای آسیب‌پذیری خیلی زیاد تا خیلی کم تقسیم شدند و نقشۀ پهنه‌بندی آسیب‌پذیری شهر تهیه شد. نتایج این پژوهش نشان می‌دهد که ناحیه‌های مرکزی 24، 13، 18، 10 و 12 آسیب‌پذیری خیلی زیاد و ناحیه‌های 19، 17، 14و 16 آسیب‌پذیری زیادی در برابر زمین‌لرزه دارند. بر همین اساس، ناحیه‌های 15، 21، 2، 6، 22، 25، 11، 20، 27 و 4 آسیب‌پذیری متوسط، ناحیه‌های 26، 9، 23، 3، 1 و 5 آسیب‌پذیری کم و ناحیه‌های 8 و 7 آسیب‌پذیری خیلی کمی در برابر زلزله دارند. | ||
| کلیدواژه‌ها | ||
| آسیب‌پذیری شهری؛ آنتروپی شانون؛ تاپسیس؛ زمین‌لرزه؛ مناطق مسکونی | ||
| عنوان مقاله [English] | ||
| Ranking Urban Residential Areas Against Earthquake Hazards Using Shannon Entropy AND Topsis Techniques (Cace Study: Amol City) | ||
| نویسندگان [English] | ||
| Parham Pahlavani1؛ Miad Badpa2 | ||
| 1Assistant Professor at School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2PhD Student, Department of Mining Exploration, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Vulnerability is the social and economic tolerance of a society against environmental hazards. Accordingly, vulnerability is the extent to which a community can respond to and deal with environmental hazards [19]. The need to reduce the earthquake’s vulnerability of the city is one of the main goals of urban planning and design. In order to reduce the vulnerability of urban buildings to earthquakes beside the possible occurrence of earthquakes, it is necessary to assess the vulnerabilities of urban areas [4, 5, 11, 15, 25]. In this regard, different conditions are simulated before the occurrence of possible earthquakes in different intensities and based on that, zoning maps of vulnerabilities of urban buildings are prepared and evaluated [5, 11]. Accordingly, entropy is an approach used to deal with disorder, instability, confusion, and doubts in a system [2]. Shannon entropy is a measure of the degree of uncertainty in the information content of a parameter that calculates the effect of each parameter on the system results [8, 10, 20, 21, 22, 24]. The TOPSIS method is used to rank and select the best option and to determine the distances between the options and their grouping [1, 6, 7, 9, 13, 16, 17]. One of the advantages of this method is that the criteria or indicators used for comparison can have different units of measurement [3, 12, 14, 18, 23, 26]. Proposed Method Shannon Entropy Shannon entropy is a function of the probability distribution and a criterion for measuring the degree of uncertainty in the information content of a parameter. By considering the frequency of subgroups’ occurrence of that parameter, it indicates the level of heterogeneity and consequently calculates the effect of each parameter on the system results. [10, 24]. TOPSIS TOPSIS method or technique for order performance by similarity to ideal solution is a multi-criteria decision making (MCDM) method. This method can be used to rank and compare different alternatives and select the best one and to determine the distances between alternatives, as well as to group them. [1, 6, 7, 9, 13, 16, 17]. One of the advantages of this method is that the criteria or indicators used for comparison can have different units of measurement and have a negative and positive nature. In other words, combination of negative and positive criteria can be used in this method [3, 12, 14, 18, 23, 26]. Results In this study, at first, the spatial layers of the study area were prepared from different sources, including the Institute of Geophysics (University of Tehran), Statistics Center of Iran, Geological Survey, Housing and Urban Development of the Mazandaran province, Amol Municipality and Regional Water Organization. This information was then implemented in the GMT software environment. These spatial layers were the energy released by the earthquakes (last 20 years in terms of tone TNT per area), the quality of buildings and structures, residential density, building density, population density, permeabilityand permeability of the road network, urban open space and groundwater depth. After implementing these layers in the city map, a decision matrix was created using the TOPSIS method. This matrix was then normalized and scaled. The MatNorm decision matrix is parametric and must be quantified. For this purpose, the weights for each index were determined. In this regard, Shannon entropy method was used for weighting. Using the relationships presented in Shannon's entropy theory, entropy values were calculated for the effective parameters in the study area. After weighting the normalized matrix, the positive ideal and negative ideal solutions were determined and the distance from the positive and negative ideals was determined and finally, we were ranked the areas by calculating the scores. According to the TOPSIS method ranking, District 24 with coefficient of proximity (0.903), District 13 with coefficient of proximity (0.727) and then District 18 with coefficient of proximity (0.694) are the most vulnerable areas of Amol city against earthquakes. . After the area rankings were implemented on the city map in GMT software, the areas were labeled based on proximity to the ideal (Figure 1). Fig. 1. Earthquake vulnerability ranking of 27 areas of Amol city Conclusion According to the zoning of the areas using Shannon entropy, the ranking of the areas, and the distance to the ideal, obtained by the TOPSIS model, and finally, the mapping of the zoning map in GMT environment, the vulnerable areas of Amol city were determined. The results showed that the central areas, i.e. 24, 13, 18, 10, and 12, are very vulnerable. Moreover, areas 19, 17, 14, and 16 are highly vulnerable, areas 15, 21, 2, 6, 22, 25, 11, 20, 27, and 4 have moderate vulnerability, areas 26, 9, 23, 3, 1, and 5 have low vulnerability, and finally, areas 8 and 7 have very vulnerable to earthquakes, respectively. Therefore, it is expected that the vulnerability of urban areas will be considered in future constructions. | ||
| کلیدواژه‌ها [English] | ||
| Urban Vulnerability, Earthquake, Residential Area, Shannon Entropy, Amol city, TOPSIS | ||
| مراجع | ||
|
[1]. حسین‌پور میل‌آغاردان، امین؛ و علی عباسپور، رحیم (1393). «بهبود نتایج پیش‌بینی وقوع زمین‌لغزش با استفاده از تئوری انتروپی شانون»، مدیریت مخاطرات محیطی، دورۀ 1، شمارۀ 2، ص 268-253. [2]. سرور، هوشنگ؛ و کاشانی اصل، امیر (1395)، "آسیب‌پذیری کالبدی شهر اهر در برابر بحران زمین‌لرزه"، فصلنامۀ آمایش محیط، دورۀ 9، شمارۀ 34. ص 108-87. [3]. Akay Ünvan, Yüksel (2020). “Financial Performance Analysis of Banks with Topsis and Fuzzy Topsis Approaches”, Gazi University Journal of Science, DOI: 10.35378/gujs.730294, ISBN: 2147-1762. [4]. Amadi, P.O.; Ikot, Akpan, N.; Thompson Ngiangia, Alalibo; Okorie, U.S.; & Rampho, Gaotsiwe, J. (2020). “Shannon entropy and Fisher information for screened Kratzer potential”, International Journal of Quantum Chemistry, DOI: 10.1002/qua.26246, ISBN: 0020-7608. [5]. Ari, Nükhet; Hankir, Mahmoud; Samak, Omar; & Topçu, Tümay (2020). “Facility Location Selection Using AHP and TOPSIS”, In Book, DOI: 10.13140/RG.2.2.22879.07849. [6]. Badawy, Ahmed; Korrat, Ibrahim; Mahmoodi, Hadid; & Gaber, H. (2015). “Probabilistic earthquake hazard analysis for Cairo, Egypt”, Journal of Seismology, 20(2), DOI: 10.1007/s10950-015-9537-5. [7]. Bayrak, Erdem; Yılmaz, Şeyda ; & Bayrak, Yusuf (2016). “Earthquake hazard analysis for the different regions in and around Ağrı”, International Conference on Advances in Natural & Applied Sciences conference, DOI: 10.1063/1.4945830. [8]. Boltürk, Eda (2020). “AS/RS Technology Selection Using Spherical Fuzzy TOPSIS and Neutrosophic TOPSIS”, In book: Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making, pp: 969-976, DOI: 10.1007/978-3-030-23756-1_115, ISBN: 978-3-030-23755-4. [9]. Firgiawan, W; Zulkarnaim, N; & Cokrowibowo, S (2020). “A Comparative Study using SAW, TOPSIS, SAW-AHP, and TOPSIS-AHP for Tuition Fee (UKT)”, IOP Conference Series Materials Science and Engineering, DOI: 10.1088/1757-899X/875/1/012088, ISBN: 1757-899X. [10]. Fortune, Timothy; & Sang, Hailin (2020), “Shannon entropy estimation for linear processes”, Journal of Risk and Financial Management, v2, pp: 82-89. [11]. Halicka, Katarzyna (2020). “Technology Selection Using the TOPSIS Method”, Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 14(1), pp: 85-96. [12]. Hanlon, Robert, T. (2020), “Shannon: entropy and information theory”, In book: Block by Block: The Historical and Theoretical Foundations of Thermodynamics, pp: 596-606, DOI: 10.1093/oso/9780198851547.003.0043, ISBN: 9780198851547. [13]. Jena, Ratiranjan; & Pradhan, Biswajeet (2020). “Integrated ANN-cross-validation and AHP-TOPSIS model to improve earthquake risk assessment”, International Journal of Disaster Risk Reduction, DOI: 10.1016/j.ijdrr.2020.101723, ISBN: 2212-4209. [14]. Kumar Tiwari, Rohit; & Kumar, Rakesh (2020). “G-TOPSIS: a cloud service selection framework using Gaussian TOPSIS for rank reversal problem”, The Journal of Supercomputing, DOI: 10.1007/s11227-020-03284-0, ISBN: 0920-8542. [15]. Liu, Yanwu; Li, Liang; Tu, Yan; & Mei, Yanlan (2020). “Fuzzy TOPSIS-EW Method with Multi-Granularity Linguistic Assessment Information for Emergency Logistics Performance Evaluation”, Symmetry, 12(8):1331, DOI: 10.3390/sym12081331, ISBN: 2073-8994. [16]. Luukka, Pasi (2020). “N—ary norm operators and TOPSIS”, 2020 IEEE International Conference on Fuzzy Systems, DOI: 10.1109/ FUZZ48607. 2020.9177580. [17]. Mayang Sari, Anggun; & Fakhrurrozi, A. (2018). “Earthquake Hazard Analysis Methods: A Review”, IOP Conference Series Earth and Environmental Science, 118(1):012044, DOI: 10.1088/1755-1315/118/1/012044. [18]. Mieszkowicz-Rolka, Alicja; & Rolka, Leszek (2020). "Preference-Oriented Fuzzy TOPSIS Method", In book: Intelligent and Fuzzy Techniques: Smart and Innovative Solutions, pp: 758-766, DOI: 10.1007/978-3-030-51156-2_88, ISBN: 978-3-030-51155-5. [19]. Nasution, Nurliana; Widi Bhawika, Gita; Wanto, Anjar; Sri Rahayu Ginantra, Ni Luh Wiwik; Afriliansyah, Teuku (2020). “Smart City Recommendations Using the TOPSIS Method”, IOP Conference Series Materials Science and Engineering, DOI: 10.1088/1757-899X/846/1/012028, ISBN: 1757-899X. [20]. Ramya, Sakkeri; & Devadas, V. (2019). “Integration of GIS, AHP and TOPSIS in evaluating suitable locations for industrial development: A case of Tehri Garhwal district, Uttarakhand, India”, Journal of Cleaner Production, 238:117872, DOI: 10.1016/j.jclepro.2019.117872. [21]. Ranjbar, Hamid Reza; & Nekooie, Mohammad Ali (2020). “An improved hierarchical fuzzy TOPSIS approach to identify endangered earthquake-induced buildings”, Engineering Applications of Artificial Intelligence, 76:21-39, DOI: 10.1016/j.engappai.2018.08.007, ISBN: 0952-1976. [22]. Rohm, Kristen; & Solouki Bonab, Vahab; & Manas-Zloczower, Ica (2020), “Quantitative evaluation of mixing using a refined Shannon entropy”, Composites Science and Technology, 197:108276, DOI: 10.1016/j.compscitech.2020.108276, ISBN: 0266-3538. [23]. Ruitenbeek, F.J.A. van; Goseling, Jasper; Bakker, Wim, H.; & Hein, Kim (2020). “Shannon Entropy as an Indicator for Sorting Processes in Hydrothermal Systems”, Entropy, 22(6):656, DOI: 10.3390/e22060656, ISBN: 1099-4300. [24]. Saha, Subhasish; & Jose, Jobin (2020). “Shannon entropy as a predictor of avoided crossing in confined atoms”, International Journal of Quantum Chemistry, DOI: 10.1002/qua.26374, ISBN: 0020-7608. [25]. Silva, Marcela do Carmo; Gomes, Carlos Francisco Simões; & Souza, Reinaldo Castro (2020). “TOPSIS-2NE’s Proposal”, International Journal of Fuzzy Systems, volume 22, pp: 1118–1122, DOI: 10.1007/s40815-020-00871-4, ISBN: 1562-2479. | ||
|
آمار تعداد مشاهده مقاله: 417 تعداد دریافت فایل اصل مقاله: 272 |
||