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مدل سازی حساسیت زمین لغزش با الگوریتم یادگیری ماشین جنگل تصادفی در آبخیز سد رئیسعلی دلواری | ||
| پژوهش های آبخیزداری | ||
| مقاله 2، دوره 33، شماره 1 - شماره پیاپی 126، بهار 1399، صفحه 2-13 اصل مقاله (644.83 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2019.126288.1219 | ||
| نویسندگان | ||
ناصر حیدری1؛ محمود حبیب نژاد2؛ عطا ا.. کاویان2؛ حمید رضا پورقاسمی  3
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| 1دکترای علوم و مهندسی آبخیزداری، دانشکده‌ی منابع طبیعی، دانشگاه علوم و کشاورزی ساری، ساری، ایران | ||
| 2استاد گروه علوم و مهندسی آبخیزداری، دانشکده‌ی منابع طبیعی، دانشگاه علوم و کشاورزی ساری، ساری، ایران | ||
| 3دانشیار 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 | ||
| آبخیز سد رئیسعلی دلواری؛ جنگل تصادفی؛ حساسیت زمین لغزش؛ میانگین کاهشی دقت | ||
| عنوان مقاله [English] | ||
| Landslide Susceptibility Modelling Using the Random Forest Machine Learning Algorithm in the Watershed of Rais-Ali Delvari Reservoir | ||
| نویسندگان [English] | ||
| Naser Heydari1؛ Mahmoud Habibnejad2؛ Ataollah Kavian2؛ Hamid Reza Pourghasemi3 | ||
| 1PhD Graduated, Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari, Iran | ||
| 2Professor, Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari, Iran | ||
| 3Associate 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 | ||
| مراجع | ||
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