پهنهبندی ریسک حریق در مناطق شهری با استفاده از روش رگرسیون لجستیک (مطالعه موردی: شهر کاشان)
محورهای موضوعی : زیرساخت اطلاعات مکانی و طبقه بندیمحمد امین وکیل الرعایا 1 , سعید ملماسی 2 , مژگان زعیم دار 3 , مهناز میرزا ابراهیمطهرانی 4
1 - دانشجوی دکترای محیط زیست – آمایش محیط زیست، دانشکده علوم و فنون دریایی، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران
2 - استادیار گروه علوم محیط زیست، دانشکده علوم و فنون دریایی، دانشگاه آزاد اسلامی واحد تهران شمال، تهران، ایران
3 - استادیار گروه علوم محیط زیست، دانشکده علوم و فنون دریایی، دانشگاه آزاد اسلامی واحد تهران شمال، تهران، ایران
4 - استادیار گروه علوم محیط زیست، دانشکده علوم و فنون دریایی، دانشگاه آزاد اسلامی واحد تهران شمال، تهران، ایران
کلید واژه: پهنهبندی ریسک آتشسوزی, رگرسیون لجستیک, تحلیل ارتباط فضایی, اعتبارسنجی, فازی,
چکیده مقاله :
پیشینه و هدف مدیریت خطر آتشسوزی یک مسئله جهانی است، جایی که سیاستهای ایمنی شهری باید این موضوع را جدی بگیرند. یکی از زمینه های پژوهش برای کنترل آتش سوزی های شهری، شناسایی نقاط بحرانی آتش سوزی در منطقه است؛ زیرا عدم شناخت کافی این نقاط باعث وقوع و گسترش آتش در مناطق و کاربریهای مختلف، تأخیر در مهار آن و وارد آمدن خسارات مالی و صدمه جانی و همچنین آلودگیهای محیطی را در پی خواهد داشت. پهنهبندی ریسک حریق باهدف بهکارگیری در برنامهریزی و مدیریت در کنترل و حرائق شهری تاکنون در منطقه مطالعاتی این تحقیق موردتوجه نبوده و در قالب طرح تحقیقاتی و مطالعاتی، پژوهشی در این زمینه صورت نگرفته است. هدف از مطالعه حاضر تعیین و شناسایی معیارهای شاخص جهت پهنهبندی ریسک آتشسوزی در منطقه موردمطالعه، ایجاد نقشه خطر آتشسوزی بر اساس روش رگرسیون لجستیک و تطابق با نقشۀ واقعیت آتش و همچنین ارائه برنامههای مدیریتی و مدیریت بحران آتشسوزی در شهر کاشان است.مواد و روش ها مراحل وتکنیکهای مورداستفاده در این تحقیق در شش گام انجام گردید. اولین گام شناسایی معیارها و شاخصهای تأثیرگذار است. با استفاده از مطالعات کتابخانهای، اطلاعات بهدستآمده از مقالات معتبر و همچنین از طریق روش دلفی بهمنظور گردآوری نظر کارشناسان از مقیاس لیکرت استفاده شد. در گام دوم غربالگری معیارها متناسب باهدف انجام گرفت که معیارهای تأثیرگذار در این تحقیق عبارتاند از عوامل آسیبپذیر شامل (تراکم جمعیت، واحدهای صنعتی، تجاری- انبار، ساختمان مرتفع، بافت قدیمی و جایگاه سوخت) و همچنین ظرفیت عوامل کاهش شامل (ایستگاه آتشنشانی، جادهها و شیرهای هیدرانت) است. در گام سوم آمادهسازی دادهها و لایهها جهت تحلیل در سیستم اطلاعات جغرافیایی صورت گرفت. در مرحله بعد به استانداردسازی لایهها با استفاده از منطق فازی پرداخته شد.در ابتدا تابع فاصله (Distance) بر روی معیارها در محیط ادریسی سلوا اجرا گردید تا فاصله از هر پدیده مشخص شود. در ادامه به روش فازی همه معیارهای تعیینشده در بازه صفرتا 255 استاندارد شدند. نوع تابع استفادهشده در رویکرد منطق فازی از نوع خطی (Linear) بوده که انتخاب نوع تابع و آستانهها بر اساس مرور منابع و نظر کارشناسی انجام شد. بهمنظور تحلیل ارتباط فضایی بین حوادث آتش سوزی رخداده در سطح شهر و نقش فاکتورهای مؤثر در وقوع آن تمامی نقاط آتش سوزی 10 سال گذشته در سطح شهر از سال 1389 تا سال 1399 استخراج و به نقشه رستری تبدیل شد. در گام پنجم نقشه خطر آتشسوزی با استفاده از رگرسیون لجستیک تهیه شد که پس از مشخص شدن اعتبار مدل رگرسیون لجستیک با استفاده از شاخصهای تعیینشده، نقشه پهنهبندی ریسک حریق شهر کاشان ترسیم گردید. در گام آخر بهمنظور اعتبار سنجی مدل رگرسیون لجستیک از Chi Square, ROC و Pseudo R Square استفاده شد.نتایج و بحث مزایای استفاده از مدل رگرسیون لجستیک علاوه بر مدلسازی مشاهدهها، امکان پیشبینی احتمال تعلق هر فرد به هریک از سطوح متغیر وابسته و امکان محاسبهی مستقیم نسبت به شانس متغیرها با استفاده از حداکثر درستنمایی بیشینه ضرایب مدل است. همچنین نسبت به سایر تکنیکهای آماری، چند متغیره مانند آنالیز رگرسیون چندگانه و آنالیز تشخیصی، متغیر وابسته میتواند تنها دو متغیر داشته باشد که یکی احتمال وقوع حادثه و دیگری عدم وقوع آن است. بهمنظور تحلیل ارتباط فضایی بین حوادث آتش سوزی رخداده در سطح شهر و نقش فاکتورهای مؤثر در وقوع آن تمامی نقاط آتش سوزی 10 سال گذشته در سطح شهر از سال 1389 تا سال 1399 استخراج و به نقشه رستری تبدیل شد. خروجی مدل رگرسیون لجستیک، ضریبهایی بین صفر و یک دارد که به احتمالات بالاتر از 0.5 ارزش یک (وقوع آتشسوزی) و به احتمالات پایینتر از 0.5 ارزش صفر (عدم وقوع آتشسوزی) میدهد و بدین ترتیب نقشه بولین ریسک تولید میگردد. این احتمال پیشبینیشده در دامنه ۰ تا 1 سبب میشود تغییر لگاریتمی پیوسته باشد و خروجی مدل بهصورت یک نقشه پیشبینی مکانی احتمال تخریب ارائه شود. سپس در معادله رگرسیون لجستیک این لایه بهعنوان متغیر وابسته و پارامترهای مؤثر در پهنهبندی حریق بهعنوان متغیر مستقل معرفی گردید پس از ورود دادهها به مدل آماری رگرسیون لجستیک، با استفاده از پارامترهای مؤثر در نرمافزار IDRISI ، ضرایب مدل، استخراج گردید. پس از مشخص شدن اعتبار مدل رگرسیون لجستیک با استفاده از شاخصهای تعیینشده، نقشه پهنهبندی ریسک حریق شهر کاشان ترسیم گردید. درنهایت منطقه موردمطالعه ازنظر پتانسیل ریسک حریق به 5 کلاس بسیار کم، ریسک کم، متوسط، بسیار زیاد، ریسک زیاد تقسیم گردید. مساحت هر یک از 5 کلاس بهدستآمده به هکتار و درصد به ترتیب 8747.47، 4669.03، 132115، 1116.33، 788.96 هکتار و 90.94، 4.85، 2.19، 1.16، و 0.82 درصد به دست آمد.نتیجه گیری مقدار 0.95 بهدستآمده از راک ROC نشاندهنده همبستگی بسیار بالای بین متغیر مستقل و وابسته است. مقدار شاخص چی دو برابر با 110836.07 است؛ با توجه به اینکه مقدار آن بسیار بیشتر از مقدار آستانه تعیینشده است درنتیجه فرض صفر تمام ضرایب نیز رد میگردد. مقدار آزمودن PR2 در این پژوهش 0.47 می باشد، بنابراین مدل رگرسیون لجستیک برازش قابل قبولی را داشته است.
Background and Objective Fire risk management is a global issue, and urban safety policies must take it seriously. One of the fields of research for controlling urban fires is to identify critical fire points in the region; Insufficient knowledge of these points will cause the occurrence and spread of fire in various areas and uses, delays in controlling it, and causing financial losses and personal injury as well as environmental pollution. Fire risk zoning with the aim of being used in planning and management in urban fire control has not been considered in the study area of this research and no research has been done in this field in the form of research and study plan. The purpose of this study is to determine and identify the criteria for fire risk zoning in the study area, to create a fire risk map based on the logistic regression method and compliance with the fire reality map, and also to present fire management programs and fire crisis management in Kashan.Materials and Methods The steps and techniques used in this study were performed in six steps. The first step is to identify the effective criteria and indicators. Using library studies, information obtained from authoritative articles, and also through the Delphi method in order to collect the opinions of experts, the Likert scale was used. In the second step, the screening of criteria was done in accordance with the purpose. The effective criteria in this study are vulnerable factors including (population density, industrial units, commercial-warehouse, high-rise buildings, old tissue, and fuel station) as well as the capacity of reducing factors. (Fire station, roads, and hydrant valves). In the next step, the layers were standardized using fuzzy logic. At first, the distance function was performed on the criteria in SELVA IDRISI to determine the distance from each phenomenon. Then, by the fuzzy method, all the criteria determined in the range of zero to 255 were standardized. The type of function used in the fuzzy logic approach is linear, and the selection of the function type and thresholds was based on a review of sources and expert opinion. In order to analyze the spatial relationship between fire incidents that occurred in the city and the role of effective factors in its occurrence, all fire points of the last 10 years in the city from 2010 to 2020 were extracted and turned into a raster map. In the fifth step, a Fire hazard map was prepared using logistic regression. After determining the validity of the logistic regression model using the specified indicators, the fire risk zoning map of Kashan was drawn. In the last step, Chi-Square, ROC, and Pseudo R Square were used to validate the logistic regression model.Results and Discussion Advantages of using the logistic regression model In addition to modeling observations, it is possible to predict the probability of each person belonging to each of the levels of dependent variables and the possibility of directly calculating the odds of variables using the maximum likelihood of maximum model coefficients. Also, compared to other statistical techniques, multivariate such as multiple regression analysis and diagnostic analysis, the dependent variable can have only two variables, one is the probability of an accident and the other is its non-occurrence. In order to analyze the spatial relationship between fire incidents that occurred in the city and the role of effective factors in its occurrence, all fire points of the last 10 years in the city from 2010 to 2020 were extracted and turned into a raster map. The output of the logistic regression model has coefficients between 0 and 1, with a probability higher than 0.5 of a value of one (occurrence of fire) and a probability of less than 0.5 of a value of zero (no occurrence of fire) and thus a boolean map of risk is generated. This logarithmic change causes the predicted probability to be continuous in the range of 0 to 1, and the output of the model to be presented as a spatial prediction map of the probability of destruction. Then, in the logistic regression equation, this layer was introduced as a dependent variable and the effective parameters in fire zoning were introduced as an independent variable. After entering the data into the logistic regression statistical model, model coefficients were extracted using effective parameters in IDRISI software. After determining the validity of the logistic regression model using the specified indicators, a fire risk zoning map of Kashan was drawn. Finally, in terms of fire risk potential, the study area was divided into 5 classes: very low, low risk, medium, very high, and high risk. The area of each of the 5 classes was obtained in hectares and the percentages were 87475.47, 4669.03, 132115, 1116.33, 788.96 hectares, and 90.94, 4.85, 2.19, 1.16, 0.82 percent, respectively.Conclusion The value of 0.95 obtained from ROC indicates a very high correlation between the independent and dependent variables. The value of the qi index is twice equal to 110836.07; Since its value is much higher than the threshold value, then the null hypothesis of all coefficients is also rejected. The value of the PR2 test in this study was 0.47, so the logistic regression model had an acceptable fit.
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_||_Ahmai ardakani, M. et al (2014). Zoning of forest fire potential areas using multi-criteria decision making methods. Journal of Geography and Environmental Planning. 60 (4): p. 49-66. (in Persian).
Anton, Howard (1994), Elementary Linear Algebra (7th ed.), John Wiley & Sons, pp. 170–171 , ISBN 978-0-471-58742-2
Abedi Gheshlaghi, H., et al (2020). "GIS-based forest fire risk mapping using the analytical network process and fuzzy logic." Journal of Environmental Planning and Management 63(3): 481-499.
Borna, F., et al (2016). "Habitat potential modeling of Astragalus gossypinus using ecological niche factor analysis and logistic regression (Case study: summer rangelands of Baladeh, Nour)". Journal of RS and GIS for Natural Resources, 7 (4): 45-61 (in Persian).
Chuvieco, E. and R.G. Congalton (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote sensing of Environment. 29(2): p. 147-159.
Chuvieco, E. and J. Salas (1996).Mapping the spatial distribution of forest fire danger using GIS. International Journal of Geographical Information Science. 10(3): p. 333-345.
Chuvieco, E. et al (2010). Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling. 221(1): p. 46-58.
Diefenbach, M.A. N.D. Weinstein, and J. O'reilly (1993). Scales for assessing perceptions of health hazard susceptibility. Health education research. 8(2): p. 181-192.
Del Hoyo, L. V., et al (2011). "Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data." European Journal of Forest Research 130(6): 983-996.
Eastman, J. Ronald (2003).IDRISI Kilimanjaro: guide to GIS and image processing, Worcester, MA: Clark Labs, Clark University, pp. 328
Erfani, M., and Ehsanzade, N (2016). " Recreation suitability zoning in part of the Oman sea coast". Journal of RS and GIS for Natural Resources, 12 (1): 107-123 (in Persian).
Habibi, A. A. Sarafrazi, and S. Izadyar (2014). Delphi technique theoretical framework in qualitative research. The International Journal of Engineering and Science. 3(4): p.8-13.
Hosmer DH, Lemeshow S (1989). Applied logistic regression. Wiley series in probability and mathematical statistics. Wiley, New York, 307 p
Jobson, J.D (2012). Applied multivariate data analysis: volume II: Categorical and Multivariate Methods: Springer Science & Business Media.
Juliá, P. B., et al (2021). "Post-earthquake fire risk assessment of historic urban areas: A scenario-based analysis applied to the Historic City Centre of Leiria, Portugal." International Journal of Disaster Risk Reduction 60: 102287.
Jennings, C.R (2013). Social and economic characteristics as determinants of residential fire risk in urban neighborhoods: A review of the literature. Fire Saf. J. 62, 13–19. [CrossRef]
Kosravi, Y. Jabbari, M (2011). Basics of Geographic Information Systems (GIS) and ARC GIS10 tutorial. Azar Kelk Publications: p. 100-150. (in Persian).
Li, S.Y. Tao, G. and Zhang, L.J (2018), "Fire Risk Assessment of High-rise Buildings Based on Gray-FAHP Mathematical Model", Procedia Engineering, Vol 211 No, pp. 395-402.
Lee, S. and B. Pradhan (2006). Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Journal of Earth System Science. 115(6): p. 661-672.
Lewis, J.R (1993). Multipoint scales: Mean and median differences and observed significance levels. International Journal of Human Computer Interaction. 5(4): p. 383-392.
Langlois, André (1987). "Clark, WAV et Hosking, PL (1986) Statistical Methods for Geographers. New York, John Wiley and Sons. Cahiers de géographie du Québec 31.82: 91-92.
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