پیش ‏بینی حساسیت به فرسایش آبکندی منطقۀ سیمره براساس مدل عامل قطعیت و تعیین اهمیت عوامل مؤثر بر آن

نوع مقاله : پژوهشی

نویسندگان

1 استادیار گروه مرتع و آبخیزداری، دانشکدۀ کشاورزی، دانشگاه لرستان، خرم‏آباد

2 دانشجوی دکتری علوم و مهندسی آبخیزداری، دانشگاه لرستان، خرم ‏آباد

3 دانشجوی کارشناسی‏ ارشد مهندسی آبخیزداری، دانشگاه لرستان، خرم ‏آباد

چکیده

فرسایش آبکندی با توجه به تخریب شدید اراضی در مناطق خشک و نیمه‏خشک، یک مشکل بزرگ در مدیریت منابع طبیعی و حفاظت خاک محسوب می‏شود. بنابراین، تعیین مناطق مستعد فرسایش آبکندی و شناسایی عوامل مؤثر بر آن می‏تواند به مدیران و تصمیم‏گیران کمک کند تا خطر وقوع این فرسایش را کاهش دهند. هدف تحقیق حاضر پیش‏بینی وقوع فرسایش‏های آبکندی منطقۀ سیمرۀ استان لرستان براساس مدل عامل قطعیت و تعیین اهمیت هریک از عوامل محیطی مؤثر بر آن است. در ابتدا نقشۀ رستری متغیرهای مؤثر بر فرسایش آبکندی (ارتفاع، درجۀ شیب، جهت شیب، فاصله از رودخانه، شاخص رطوبت توپوگرافی، شاخص توان جریان، کاربری اراضی، خاک‏شناسی و سنگ‏شناسی) در قالب پایگاه داده و سامانۀ اطلاعات جغرافیایی ساخته شد. براساس مطالعات میدانی، 100 موقعیت فرسایش آبکندی ثبت و به‌صورت تصادفی دو گروه آموزش مدل (70 درصد آبکند‏ها) و اعتبارسنجی مدل (30 درصد آبکند‏ها) تقسیم شد. پس از محاسبۀ شاخص‏های CF و Z مدل عامل قطعیت و واسنجی مدل، نقشۀ پیش‏بینی مناطق مستعد فرسایش آبکندی با استفاده از نرم‌افزارهای ArcGIS10.2 تهیه شد. نقشۀ نهایی براساس روش منحنی مشخصۀ عامل گیرنده (ROC) و داده‌های آبکندی گروه اعتبارسنجی، ارزیابی و اعتبارسنجی شد. نتایج اعتبارسنجی نقشۀ پیش‏بینی مناطق مستعد فرسایش آبکندی نشان داد که دقت مدل عامل قطعیت6/85 درصد است. بنابراین، کارایی مدل عامل قطعیت برای پیش‏بینی مناطق مستعد وقوع فرسایش آبکندی تأیید شد. علاوه بر آن، نتایج آنالیز حساسیت مدل نشان داد که متغیرهای خاک‏شناسی، سنگ‏شناسی و شیب زمین بیشترین تأثیر را بر دقت پیش‏بینی وقوع فرسایش آبکندی دارند.
 
 
 
 
 
 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of gully erosion susceptibilityin Seimare region using certainty factor model and importance analysis of conditioning factors

نویسندگان [English]

  • Naser Tahmasebipoor 1
  • Omid Rahmati 2
  • Samira Ghorbani Nejad 3
1 Department of watershed management engineering, Faculty of Agriculture, Lorestan University, Khoramabad
2 Ph.D Student in watershed management engineering and sciences, Lorestan University, Khormabad
3 M.Sc. Student in watershed management engineering, Lorestan University, Khormabad
چکیده [English]

Gully erosion constitutes a major problem in natural resources management and soil conservation, which causes severe land degradation in arid and semi-arid areas. Therefore, determination of gully prone area and identification of gully conditioning factors can help to managers and decision makers to reduce the hazard of gully erosion. The aim of this study is to predictthe gully erosion susceptibility usingcertainty factor (CF) model and importance analysis of gully conditioning factors in the Seimare region, Lorestan province.At first, the raster dataset of gully erosion conditioning factors (e.g. altitude, slope degree, slope aspect, distance from river, topographic wetness index, stream power index, landuse, soil and lithology) was created using geographic information system (GIS).By means of field surveys, a total of 100 gully locations were identified and these locations were divided into two groups (1) training of the model (70% gullies), and (2) validation of the model (30% gullies).After calculation of CF and Z indices of certainty factor model and model calibration, the gully erosion susceptibility map was prepared using ArcGIS10.2. The validation of the gully susceptibility map was conducted based on the receiver operating characteristic (ROC) curve method, and validation dataset. The resulting gully susceptibility maps showed 85.6% accuracy.Therefore, it was established in this study that the CF model is promising of make accurate and reliable spatial prediction of gully susceptibility.Furthermore, the result of sensitivity analysis indicated that soil, lithology, and slope angle are most important factors in gully susceptibility prediction.
 
 

کلیدواژه‌ها [English]

  • Probability of gully occurrence
  • Conditional probability
  • Sensitivity analysis
  • Spatial prediction
  • Seimare region (Lorestan)
 
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