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پهنه بندی حساسیت سیلگیری با استفاده از روش ترکیبی نوین تئوری بیزین‌ـ فرایند تحلیل سلسله‌مراتبی (مطالعۀ موردی: حوضۀ آبخیز نکا ـ استان مازندران) | ||
| اکوهیدرولوژی | ||
| مقاله 13، دوره 4، شماره 2، تیر 1396، صفحه 447-462 اصل مقاله (693.26 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2017.61481 | ||
| نویسندگان | ||
| علیرضا عرب عامری1؛ حمیدرضا پورقاسمی* 2؛ کورش شیرانی3 | ||
| 1دانشجوی دکتری ژئومورفولوژی دانشگاه تربیت مدرس، تهران، ایران. | ||
| 2استادیار بخش مهندسی منابع طبیعی و محیط زیست، دانشکدۀ کشاورزی، دانشگاه شیراز، شیراز، ایران. | ||
| 3استادیار، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اصفهان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اصفهان، ایران. | ||
| چکیده | ||
| تهیۀ نقشۀ حساسیتپذیری سیلاب، نخستین گام در برنامههای مدیریت سیلاب است. هدف از این پژوهش، شناسایی مناطق حساس به سیلگیری با استفاده از روش ترکیبی نوین تئوری بیزین فرایند تحلیل سلسله‌مراتبی (Bayes-AHP) در حوضۀ آبخیز نکاـ شهرستان ساری است. به‌منظور تهیۀ نقشۀ حساسیتپذیری سیلگیری در منطقۀ مطالعاتی، نقشۀ پراکنش سیلابها به‌منظور تحلیلهای آماری تهیه شد. از تعداد کل ۳۴۲ موقعیت سیل، ۷۰ درصد (۲۴۰ موقعیت سیل) به‌منظور اجرای مدل و ۳۰ درصد (۱۰۲ موقعیت سیل) به‌منظور اعتبارسنجی استفاده شد. با استفاده از مطالعۀ گذشته و پیمایشهای گستردۀ میدانی، ۱۱ عامل مؤثر شامل درصد شیب، طبقات ارتفاعی، فاصله از آبراهه، تراکم زهکشی، شاخص پوشش گیاهی تفاضلی نرمالشده (NDVI)، سنگشناسی، کاربری اراضی، شاخص رطوبت توپوگرافی (TWI)، شاخص توان آبراهه (SPI)، بارندگی سالانه و انحنای سطح به‌منظور پهنهبندی سیلگیری بررسی شد. با استفاده از روش AHP، وزن هر یک از عوامل و بر اساس تئوری بیزین وزن هر یک از طبقات عوامل مؤثر بر وقوع سیلابهای منطقۀ مطالعه‌شده محاسبه شد. درنهایت، نقشۀ پهنهبندی حساسیتپذیری سیلگیری در پنج طبقه و در محیط نرمافزار ArcGIS10.1 تهیه شد. به‌منظور ارزیابی مدل منحنی تشخیص عملکرد نسبی (ROC) استفاده شد. نتایج ارزیابی نشان داد مدل ترکیبی دقت مناسبی (۷۶۱/۰) در شناسایی پهنههای حساس به سیلاب دارد. بر اساس نتایج بهدستآمده، عوامل درصد شیب، ارتفاع و کاربری اراضی بهترتیب با وزنهای۲۶۰/۰، ۱۹۵/۰ و ۱۴۶/۰ بیشترین تأثیر را در وقوع سیلابهای منطقۀ مطالعاتی داشتهاند. همچنین طبق نتایج، ۲۴/۱۷ و ۳۷/۱۵ درصد از حوضۀ آبخیز نکا در ردههای حساسیت زیاد و بسیارزیاد قرار گرفته است. مدل ترکیبی ارائه‌شده میتواند برای تحقیقات بیشتر در زمینۀ تهیۀ نقشۀ خطر سیلگیری و مدیریت بحران استفاده شود. | ||
| کلیدواژه‌ها | ||
| اعتبارسنجی؛ پهنه بندی؛ تئوری بیزین؛ حوضۀ آبخیز نکا؛ فرایند تحلیل سلسله‌مراتبی | ||
| عنوان مقاله [English] | ||
| Flood susceptibility zonation using new ensemble Bayesian-AHP methods (Case study: Neka Watershed, Mazandaran Province) | ||
| نویسندگان [English] | ||
| Alireza Arab Ameri1؛ Hamid Reza Pourghasemi2؛ Kourosh Shirani3 | ||
| 1PhD Candidate of Geomorphology, Tarbiat Modares University, Tehran, Iran | ||
| 2Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran | ||
| 3Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran | ||
| چکیده [English] | ||
| Flood susceptibility mapping is the first step in flood management programs. Flood prediction can help reduce its following damages. The main objective of this study is identification of prone areas to flooding using new ensemble Bayesian-AHP methods in the Neka-Sari watershed, Iran. Flood inventory map was prepared based on statistical analyses. A total of 240 (70 %) and 102 (30 %) out of 342 observed events were used as training and validation data set, respectively. Based on literature review and extensive field studies, a total of 11 parameters in relation to flood occurrences were selected for flood mapping, including slope percent, elevation, distance to river, drainage density, NDVI, lithology, land use, topography wetness index (TWI), stream power index (SPI), rainfall, and curvature. The weights of each factor were determined by AHP method. Also, the relation between factor classes and flood events and the weight of each class were estimated using Bayesian theory. Finally, by integration of factors and their classes in ArcGIS, flood susceptibility map was obtained with five classes. In order to evaluate the obtained model, ROC curve was employed. Results showed that the ensemble model had a high accuracy (76.10 %) in flood susceptibility mapping. Also, slope percent, elevation, and land use had the highest effect on flood events with values of 0.260, 0.195, and 0.146, respectively. According to the results, 24.17 and 37.15 % of the study area are categorized in high and very high susceptibility classes, respectively. The presented combined model can be used for further studies on natural hazard mapping and disaster management. | ||
| کلیدواژه‌ها [English] | ||
| Zonation, validation, Bayesian theory, Analytical Hierarchy Process (AHP), Neka Watershed | ||
| مراجع | ||
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