مدل‌سازی و پهنه‌بندی حساسیت به زمین‌لغزش مناطق جنگلی به‌منظور طراحی مسیر جاده جنگلی با استفاده از سامانه استنتاج عصبی- فازی تطبیقی

نوع مقاله : علمی- پژوهشی

نویسندگان

1 استادیار، دانشگاه گیلان

2 دانشیار، دانشگاه تربیت مدرس

چکیده

پژوهش پیش‌رو با استفاده از سیستم استنتاج عصبی- فازی تطبیقی و سیستم اطلاعات جغرافیایی و باتوجه به ویژگی‌های فیزیوگرافی زمین به ارائه مدلی می‌پردازد که قادر به برآورد حساسیت به زمین‌لغزش برای طراحی کم‌ لغزش‌ترین مسیرهای جاده جنگلی باشد. با استخراج شش عامل شیب، جهت، زمین‌شناسی، شکل شیب، فاصله از رودخانه و فاصله از گسل در نقاط لغزشی برداشت‌شده در سطح منطقه موردمطالعه و با استفاده از سیستم استنتاج عصبی- فازی تطبیقی مدل ساخته شد. نتایج شاخص‌های آماری بهترین مدل، ضریب‌تبیین 73/0 و مجذور میانگین مربعات خطای 26/0 را نشان داد. یافته‌های تحلیل حساسیت مدل نشان داد که مهمترین عامل‌های مؤثر در ایجاد حساسیت به زمین‌لغزش به‌ترتیب فاصله از رودخانه‌های اصلی، نوع تشکیلات زمین‌شناسی، شیب زمین، شکل زمین، فاصله از گسل و جهت جغرافیایی بوده‌اند. ارزیابی جاده‌های موجود ازنظر میزان عبور از عرصه‌های حساس به زمین‌لغزش طبق برآورد مدل نشان داد بیشترین سطح جاده‌ها روی طبقات حساسیت «متوسط» و «زیاد» قرار گرفته است.

کلیدواژه‌ها

عنوان مقاله [English]

Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning

نویسندگان [English]

  • Ismaeil Ghajar 1
  • Akbar Najafi 2
1 Associate Prof., Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Noor, I.R. Iran
چکیده [English]

This study presents landslide susceptibility (LS) prediction model using the Adaptive Neuro Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) which incorporates the physiographic information. Such models are is useful for forest road planning. To this aim, a set of factors including the terrain slope, aspect, geology formation, curvature, distance to rivers, and distance to faults at occurred landslide points were integrated into the ANFIS model. The modeling using a subtractive clustering method returned a coefficient of determination (R2) of 0.73 and a root mean square error (RMSE) of 0.27 for the best model. The sensitivity analysis indicated the distance to the rivers, geology formation, terrain slope, curvature, distance to the faults, and aspect as the most effective factors on the landslide occurrence. Furthermore, an evaluation of existing roads on simulated LS map showed that the majority of the currently existing roads are located on “medium” and “high” LS classes.

کلیدواژه‌ها [English]

  • Landslide susceptibility
  • Neuro-fuzzy
  • model
  • ANFIS
  • forest road

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