ارزیابی امکان روی‌داد سیل در آبخیز رود تلوار با مدل های وزن شاهد و وایازی پشتیبان بیزین

نوع مقاله : پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران

2 استادیار گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان، سنندج، ایران

3 دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی، دانشگاه کردستان، سنندج، ایران

4 استادیار بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان کردستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، سنندج، ایران

چکیده

سیل یکی از خطرناک ­ترین و شایع ­ترین پیش‌آمد‌های طبیعی در جهان است که هر ساله باعث رسیدن زیان­ ها و آسیب­ های بسیاری بر جامعه و محیط زیست می‌شود­. ارزیابی و مدیریت جامع سیلاب برای کم کردن تاثیر سیل بر زندگی و گذران مردم ضروری است. افزایش سیل در سال­ های اخیر نشان می‌دهد که مکان یابی و شناسایی منطقه‌های سیل­­خیز برای پیش ­­بینی این فاجعه‌ی زیست محیطی اهمیت زیادی دارد زیرا نشناختن منطقه‌های حساس به سیل در آبخیز ممکن است اثر ویرانگری در پی داشته باشد. هدف اصلی این پژوهش ارزیابی عمل‌کرد مدل وزن­ شاهد و مدل وایازی پشتیبان بیزین برای تهیه‌ی نقشه‌ی حساسیت سیل در آبخیز رود تلوار استان کردستان است. 93 جای سیل‌گیر مشخص‌شده در منطقه به‌شیوه‌ی تصادفی به گروه واسنجی (70 %) و گروه اعتبارسنجی (30 %) تقسیم شد. هر دو مدل بر 10 عامل مؤثر در ایجاد سیل تمرکز دارند، که عبارت است از شیب، جهت شیب، انحنا، مدل رقومی ارتفاع، فاصله از آب‌راه، شاخص رطوبت پستی‌بلندی، شاخص توان آب‌راه، بارندگی، زمین‌شناسی و کاربری زمین. بر پایه‌ی مدل وزن شاهد 35/8% از سطح منطقه در طبقه‌ی خطر متوسط تا خیلی زیاد، و برپایه‌ی مدل وایازی پشتیبان بیزین 45/08% از سطح منطقه در طبقه‌ی خطر متوسط تا خیلی زیاد بود. برای اعتبارسنجی نقشه­ های توان سیل­ خیزی، از منحنی تشخیص عمل‌کرد نسبی بهره‌گرفته شد. اگرچه هر دو مدل دقت کافی دارد، در مدل وایازی پشتیبان بیزین درستی بیش‌تری (93/4 %) دیده شد. در زمینه‌ی توان­یابی خطر سیل کار‌کرد مدل وایازی پشتیبان بیزین بهتر از مدل وزن­ شاهد بود.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation of the Flooding Susceptibility in the Telvar River Basin Using the Evidence Weight Models and the Bayesian Logistic Regression

نویسندگان [English]

  • Afrooz Gavili 1
  • Jamil Bahrami 2
  • Kamran Chapi 3
  • Omid Rahmati 4
1 Graduate student in Civil Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
2 Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
3 Associate Professor, Department of Nature Engineering, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
4 Assistant Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
چکیده [English]

Flooding is one of the most dangerous and common natural disasters, which yearly causes many damages and causalities for communities and the environment. A comprehensive flooding assessment and management is essential to reduce the effects of flooding on people's lives and livelihoods. Due to an increase in flooding in recent years, locating and identifying flood-prone areas is very important to predict this environmental disaster as mis-identification of flood-prone areas in a watershed may have devastating effects. The main purpose of this study was to evaluate the performance of the Bayesian Logistic Regression (BLR) and the weight-of-evidence (WOE) models for preparing flooding susceptibility maps in the Talvar Watershed of Province of Kurdistan. The geographical location of 93 flood prone areas identified in the watershed was randomly divided into one group (70%) for calibration and another (30%) for validation. Both models consider ten effective factors in causing flooding, namely: slope, slope direction, curvature, digital elevation model, distance from stream, topographic wetness index (TWI), stream power index (SPI), rainfall amount, geology, and land use. According to the WOE model, about 35.8% of the area is placed in the medium to very high hazard class, and based on the Bayesian logistic regression model, about 45.08% of the area is placed in the medium to very high hazard class. The relative performance detection curve was used to validate the flooding potential maps. Even though both models offered sufficient accuracy, the higher accuracy was assigned to the BLR model (93.4%). Therefore, the BLR model has better performance than the WOE model in terms of the flooding risk potential.

کلیدواژه‌ها [English]

  • Bayesian logistic regression mode
  • Talvar watershed
  • flood

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  • تاریخ دریافت: 20 فروردین 1400
  • تاریخ بازنگری: 02 خرداد 1400
  • تاریخ پذیرش: 30 خرداد 1400

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