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ارائۀ مدل پیش‌بینی کنندۀ آسیب‌پذیری کالبدی محلات در برابر زلزله با استفاده از یادگیری ماشین (مطالعه‌ی موردی: منطقۀ 1 شهرداری تهران) | ||
| مطالعات ساختار و کارکرد شهری | ||
| مقاله 8، دوره 10، شماره 37، 1402، صفحه 195-226 اصل مقاله (1.14 M) | ||
| نوع مقاله: مقالات مستقل پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22080/usfs.2023.25302.2350 | ||
| نویسندگان | ||
| مریم محمدی* 1؛ مرجان وثوقی نیا2 | ||
| 1دانشیار گروه طراحی شهری، دانشکده معماری و شهرسازی دانشگاه هنر، تهران، ایران. | ||
| 2گروه طراحی شهری، دانشکدۀ معماری و شهرسازی، دانشگاه هنر، تهران، ایران | ||
| تاریخ دریافت: 05 اردیبهشت 1402، تاریخ بازنگری: 05 شهریور 1402، تاریخ پذیرش: 09 مهر 1402 | ||
| چکیده | ||
| مدیریت بحران هوشمند (در سه مرحلۀ قبل، حین و پس‌ازآن)، با تأکید بر آمادگی و پیش‌بینی آسیب‌پذیری در برابر زلزله، امکان پیش‌بینی، کاهش آسیب‌پذیری و افزایش قدرت در تصمیم-سازی را فراهم می‌آورد. این مقاله بر آن است تا با استفاده از یادگیری ماشین، به ارائۀ مدل پیش-بینی‌کنندۀ آسیب‌پذیری کالبدی در برابر زلزله بپردازد. روش پژوهش کمی است. دادههای ارائه‌شده به ماشین برای آموزش و تست، مربوط به پهنه‌های محلات منطقۀ 1 شهرداری تهران بوده‌اند (که در محدودۀ خطر گسل شمال تهران قرار دارند). ویژگی‌های مورد تأکید که ماشین براساس آنها آموزش دیده تا مدل پیش‌بینی‌کننده را ارائه دهد، مشتمل بر موارد زیر هستند: ویژگی‌های الگوی قطعات و ساختار ابنیه، الگوی معبر، کاربری اراضی و موقعیت نسبت به گسل اصلی و فرعی بوده‌اند. مجموعۀ داده‌ها مشتمل بر 1997 سطر و 26 ستون بوده است. برخی از داده‌ها از جی.آی.اس. منطقه استخراج و بخش دیگری از داده‌ها از تحلیل نقشۀ پهنه‌ها به دست آمد. با توجه به بهره‌گیری از رویکرد یادگیری ماشین نظارت‌شده، برچسب‌گذاری توسط محققان در پنج طیف انجام شد. برای آموزش ماشین از الگوریتم درخت تصمیم، ماشین بردار پشتیبان و شبکۀ عصبی چندلایه استفاده شد. حجم دادههای آموزش به تست 70 به 30 در نظر گرفته شد. با بررسی دقت مدل توسط ماتریس درهم‌آمیختگی، مشخص شد که الگوریتم درخت تصمیم با دقت 99.50، حساسیت 99.42 و خطای 0.5 دارای عملکرد بهتری نسبت به دو الگوریتم دیگر است. شبکۀ عصبی نیز با دقت 97.85، حساسیت 97.57 و خطای 2.15، دارای عملکرد مناسبی است. بررسی میزان اعتمادپذیری مدل پیش‌بینی کننده با دادههای مربوط به محلۀ جوانمرد قصاب در منطقۀ 20 نیز نشان داد که ماشین آموزش‌دیده، با دقت بالای 97 درصد قابلیت پیش‌بینی پذیری دارد. بدین‌ترتیب ماشین آموزش‌دیده با دقت و سرعت بالا می‌تواند به پیش‌بینی میزان آسیب‌پذیری بافت‌های کالبدی در برابر زلزله بپردازد. | ||
| کلیدواژه‌ها | ||
| مدل پیش‌بینی کننده؛ آسیب پذیری؛ یادگیری ماشین؛ مورفولوژی؛ زلزله | ||
| عنوان مقاله [English] | ||
| Presenting a Predictive Model of the Physical Vulnerability of Neighborhoods against Earthquakes (Case Study: District 1 of Tehran Municipality) | ||
| نویسندگان [English] | ||
| Maryam Mohammadi1؛ Marjan Voosooghi Nia2 | ||
| 1Associate Professor, Department of Urban Design, Faculty of Urban Planning and Architecture, University of Art, Iran | ||
| 2M.A. in Urban Design, Department of Urban Design, Faculty of Urban Planning and Architecture, University of Art, Iran | ||
| چکیده [English] | ||
| The issue of the physical vulnerability of neighborhoods against earthquakes is the subject of this research. Crisis management and smart crisis management are generally considered in three stages: before, during, and after the crisis. The management of a smart crisis in all three stages, with emphasis on preparedness and anticipation of vulnerability against disasters such as earthquakes, can provide a way to predict vulnerability and increase power in decision-making. The purpose of this research is to present a predictive model for the vulnerability of the physical context against earthquakes in District 1 of Tehran municipality using machine learning. The research method is analytical and quantitative. Some of the data was collected from GIS and some were extracted from the map analysis. According to the use of a supervised learning approach in this research, labeling was performed by researchers in five different spectrums. Decision tree algorithm, support vector machine (SVM), and multilayer neural network (MLP) were used as machine learning algorithms. The portion of training data for the test was considered to be 70 to 30. By examining the accuracy of the model by the confusion matrix, it was found that the decision tree algorithm with an accuracy of 99.50, sensitivity of 99.42, and error of 0.5 has better performance than the other two algorithms. Moreover, the neural network with an accuracy of 97.85, sensitivity of 97.57, and error of 2.15 showed better performance than support vector machine. | ||
| کلیدواژه‌ها [English] | ||
| Predictive model, Vulnerability, Machine learning, Morphology, Earthquake | ||
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آمار تعداد مشاهده مقاله: 171 تعداد دریافت فایل اصل مقاله: 156 |
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