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ارزیابی کارآیی مدل های پیش بینی حساسیت وقوع زمین لغزش در آبخیز بار نیشابور | ||
| پژوهش های آبخیزداری | ||
| مقاله 3، دوره 33، شماره 2 - شماره پیاپی 127، تیر 1399، صفحه 20-30 اصل مقاله (803.77 K) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2019.126950.1241 | ||
| نویسندگان | ||
| مریم حلاجی* 1؛ محمدعلی زنگنه اسدی2؛ ابوالقاسم امیراحمدی3 | ||
| 1دانشجوی دکتری ژئومورفولوژی دانشگاه حکیم سبزواری | ||
| 2دانشیار گروه آب و هواشناسی و ژئومورفولوژی دانشگاه حکیم سبزواری | ||
| 3استاد گروه آب و هواشناسی و ژئومورفولوژی دانشگاه حکیم سبزواری | ||
| چکیده | ||
| زمینلغزش یکی از انواع ویرانگر فرسایش در دامنهها است که موجب واردشدن زیانهای مالی و جانی فراوانی میشود. شناسایی عاملهای موثر در رخداد زمینلغزش و تهیهی نقشهی پهنهبندی حساسیت آن از ابزارهای اساسی کاهشدادن زیانهای احتمالی است. در این مقاله خطر زمینلغزش در آبخیز بار نیشابور با روشهای ماشین بردار پشتیبان، بیشینهی آنتروپی و الگوریتم جنگل تصادفی پهنهبندی شد. با جمعآوری اطلاعات از پراکندگی زمینلغزشها در منطقه، نقشهی پراکنش زمینلغزشها، و 12 لایهی اطلاعاتی شامل درجهی شیب، جهت شیب، انحنای سطح، انحنای نیمرخ، ارتفاع، کاربری زمین، زمینشناسی، فاصله از جاده، فاصله از آبراه، فاصله از گسل، شاخص رطوبت پستیوبلندی و تراکم زهکشی در سامانهی اطلاعات جغرافیایی تهیه شد. نقشهی حساسیت زمینلغزش منطقه با سه روش جنگل تصادفی، بیشینهی آنتروپی و ماشین بردار پشتیبان تهیه و با منحنی تشخیص عملکرد نسبی و 30% نقطههای لغزشی بهکارنرفته در فرآیند مدلسازی، صحتسنجی شد. نتیجهی ارزیابی مدلها نشان داد که دقت نقشههای برآوردشده با روشهای ماشین بردار پشتیبان، بیشینهی آنتروپی و الگوریتم جنگل تصادفی بهترتیب 86، 75 و 82% است. نقشههای دادهشده ممکن است در شناخت منطقههای ناپایدار و نیز در اجرای برنامههای عمرانی بهخصوص جادهسازی نقش بسزایی داشته باشد. توانمندی گردشگری در حوزهی بار زیاد است، و توجه به امکان زمینلغزش در آن ضروری بهنظر میرسد. | ||
| کلیدواژهها | ||
| حساسیت زمینلغزش؛ پهنهبندی؛ جنگل تصادفی؛ ماشین بردار پشتیبان؛ منحنی تشخیص عملکرد نسبی | ||
| عنوان مقاله [English] | ||
| An Assessment of the Landslide Susceptibility Prediction Models in the Bar Watershed- Neyshabur | ||
| نویسندگان [English] | ||
| Maryam Hallaji1؛ MohamadAli Zangane Asadi2؛ Abolghasem Amirahmadi3 | ||
| 1PhD., Student of Geomorphology- Department of Physical Geography- Hakim Sabzevari University | ||
| 2Associate Professor- Department of Physical Geography- Hakim Sabzevari University | ||
| 3Professor- Department of Physical Geography- Hakim Sabzevari University | ||
| چکیده [English] | ||
| Landslide is one of the most destructive types of erosion of slopes, causing substantial financial losses. Identification of causative factors in the landslide occurrence and providing a zoning map of the susceptible areas is one of the basic tools for minimizing the possible damages. In this research, the landslide susceptibility map in the Neyshabur Watershed was prepared using three algorithm including, Support Vector Machine, Maximum Entropy, and Random Forest Algorithm. A map of landslide distribution was prepared along with 12 thematic layers including slope, aspect, plan curvature, profile curvature, elevation, land use, geology, distance from the road, distance from the rivers, distance from the fault, topographic wetness index, and drainage density in the GIS environment. The landslide susceptibility map of the studied area was prepared using three methods of random forest algorithm, maximum entropy, and support vector machine algorithm, and using Receiver Operating Characteristics and 30% of unused landslide points in the modeling process. The results of an assessment of the models indicated that the accuracy of the estimated maps prepared by the Support Vector Machine, Maximum Entropy, and Random Forest Algorithm were 86, 75, and 82 percent, respectively. Therefore, it can be stated that the presented maps can play an important role in identifying the slide-prone areas as well as in the implementation of development plans, especially road construction in the studied area. Given the potential of tourism in the catchment area, it is necessary to pay attention to the landslide potential in the catchment. | ||
| کلیدواژهها [English] | ||
| Landslide susceptibility, zoning, random forest, support vector machine, receiver operating characteristics curve | ||
| مراجع | ||
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