مدل سازی حساسیت زمین لغزش با الگوریتم یادگیری ماشین جنگل تصادفی در آبخیز سد رئیسعلی دلواری

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دکترای علوم و مهندسی آبخیزداری، دانشکده‌ی منابع طبیعی، دانشگاه علوم و کشاورزی ساری، ساری، ایران

2 استاد گروه علوم و مهندسی آبخیزداری، دانشکده‌ی منابع طبیعی، دانشگاه علوم و کشاورزی ساری، ساری، ایران

3 دانشیار بخش مهندسی منابع طبیعی و محیط زیست، دانشکده‌ی کشاورزی، دانشگاه شیراز، شیراز، ایران

چکیده

هدف از این پژوهش مدل­سازی‌کردن مکانی حساسیت زمین­لغزش با الگوریتم یادگیری ماشین جنگل تصادفی و اولویت‌بندی کردن عامل‌های موثر بر وقوع آن در آبخیز سد رئیسعلی دلواری است. نقشه‌ی پراکنش زمین­لغزش­های منطقه با بازدیدهای صحرایی و بانک اطلاعات زمین‌لغزش­های کشور تهیه شد. در مجموع از 279 زمین‌لغزش شناخته‌شده 70% (195) آن برای مدل‌سازی و 30% (84) مانده برای ارزیابی مدل به‌کاربرده شد. لایه‌های اطلاعاتی ارتفاع، جهت شیب، درجه‌ی شیب، انحنای سطح، انحنای نیمرخ، شاخص رطوبت پستی‌وبلندی، فاصله از شبکه‌ی آب‌راه‌، تراکم زه‌کشی، فاصله از گسل، فاصله از جاده، زمین‌شناسی و شاخص تفاضلی پوشش گیاهی بهنجار­شده انتخاب شد. مدل جنگل تصادفی بر اساس ارتباط بین متغیر وابسته (زمین­لغزش­ها) و متغیرهای مستقل (عامل‌های موثر) در نرم­افزار R و با بسته‌ی نرم‌افزاری Random Forest اجرا، و نقشه‌ی حساسیت زمین­لغزش تهیه شد. مدل با به‌کاربردن منحنی تشخیص عمل‌کرد نسبی و 30% از داده­های لغزشی به‌کاربرده‌نشده در فرآیند مدل­سازی ارزیابی شد. نتایج ارزیابی نشان‌دهنده‌ی دقت عالی مدل جنگل تصادفی 0/983 (3/98%) بود. اولویت­بندی عامل‌های موثر اهمیت درجه‌ی شیب، ارتفاع، انحنای نیمرخ، فاصله از جاده و واحدهای سنگ‌شناسی را نشان‌داد. بنابراین به‌نظر می‌رسد که نقشه‌ی حساسیت زمین‌لغزش تهیه‌شده ممکن است نقش بسزایی در تصمیم‌گیری­های مدیران برای آمایش‌کردن سرزمین و مدیریت‌کردن جامع آبخیز سد رئیسعلی دلواری داشته باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Landslide Susceptibility Modelling Using the Random Forest Machine Learning Algorithm in the Watershed of Rais-Ali Delvari Reservoir

نویسندگان [English]

  • Naser Heydari 1
  • Mahmoud Habibnejad 2
  • Ataollah Kavian 2
  • Hamid Reza Pourghasemi 3
1 PhD Graduated, Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari, Iran
2 Professor, Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari, Iran
3 Associate Professor, Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran
چکیده [English]

The aim of this study was to model the landslide susceptibility using the Random Forest Machine learning technique and prioritization of effective factors on landslide occurrence in the watershed of the Rais-Ali Delvari Reservoir. The landslide inventory map was prepared using extensive field surveys and the Iranian Landslides Working Party Data Bank. Of the total of 279 identified landslide locations, 70% were used for the modelling processes and the remaining (30%) were applied for validation of the developed model. Different thematic layers including elevation, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), distance from rivers, drainage density, distance from faults, distance from roads, lithological units, and the normalized difference vegetation index (NDVI) were selected. According to the relationship between the dependent (landslides) and the independent (effective factors) variables in the R statistical software, the random forest algorithm was run using the “Random Forest” package, and a landslide susceptibility map was prepared. Accuracy of the model was tested using the receiver operating characteristic (ROC) curve based on 30% of unused landslides in the modelling process. Accuracy results indicated that the Random Forest model with an AUC value of 0.983 had an excellent precision. Also, prioritization of the effective factors showed that the slope angle, elevation, plan curvature, distance from road, and lithological units had the highest effect on landslide occurrence. Therefore, it maybe suggested that the prepared landslide susceptibility map could be effective in decision making for land use planning, and in the managing of the Rais-Ali Delvari Reservoir Watershed.

کلیدواژه‌ها [English]

  • Landslide susceptibility
  • Mean decrease accuracy
  • random forest
  • Rais-Ali Delvari Reservoir Watershed
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