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شبیه سازی حساسیت زمین لغزش با مدل های داده‌کاوی در منطقه رابُر، استان کرمان | ||
| پژوهش های آبخیزداری | ||
| مقاله 9، دوره 35، شماره 2 - شماره پیاپی 135، تیر 1401، صفحه 101-118 اصل مقاله (2.18 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmrj.2021.356462.1439 | ||
| نویسنده | ||
| الهام رفیعی ساردویی* | ||
| دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی، دانشگاه جیرفت، کرمان، ایران | ||
| چکیده | ||
| زمینلغزش یکی از رویدادهای طبیعی مهم است که هر ساله موجب زیانهای مالی و تخریب اندوخته‌های طبیعی میشود. منطقه‌ی رابُر در سرآب آبخیز هلیل‌رود به‌دلیل بودنِ سازندهای با خاک رُس در آن آماده‌ی زمینلغزش است، و این موجب وارد شدن اندازه‌های زیادی از مواد معلق به سد صفا در منطقه‌ی رابُر شده است. هدف از این پژوهش پهنهبندی این رویداد زیست‌محیطی با مدلهای شبکه‌ی عصبی پیچشی، تابع شاهد قطعی، و ماشین بردار پشتیبان در رابُر است. عاملهای ارتفاع، شیب، فاصله از گسل، زمینشناسی، کاربری زمین، نوع خاک، پوشش گیاهی، فاصله از رود، و بارندگی به‌کار برده‌شد. نقشه‌ی پراکنش زمینلغزشها در جایگاه متغیر وابسته با دادههای سازمان زمینشناسی و بازدیدهای میدانی با بهره‌گیری از جی‌پی‌اس تهیه شد. از 70 زمینلغزش، 49 زمینلغزش (70%) برای شبیهسازی و 21 زمینلغزش (30%) برای اعتبارسنجی مدل به‌کار برده‌شد. نتیجه‌ی اعتبارسنجی مدلها با منحنی ROC نشان داد که اندازه‌های سطح زیر منحنی برای مدلهای شبکه‌ی عصبی پیچشی، ماشین بردار پشتیبان، و تابع شاهد قطعی به ترتیب 0/987، 0/958 و 0/899 است. به طور کلی، نتیجه‌ها هم‌خوانی رضایت‌بخشی میان دادههای زمینلغزش در منطقه و نقشههای حساسیت زمینلغزش نشان داد، و کارکرد مدل یادگیری عمیق شبکه‌ی عصبی پیچشی بیش‌تر از دو مدل دیگر بود. نقشه‌ی حساسیت زمینلغزش در چهار رده با حساسیت کم، متوسط، زیاد و خیلی زیاد رده‌بندی شد. بر پایه‌ی نتیجه‌ی خروجی هر سه مدل بخشهای مرکزی، جنوب شرقی و جنوب غربی منطقه در خطر زیاد و خیلی زیاد زمینلغزش است. انجام طرحهای مناسب مانند دیوارهای حائل، جلوگیری از نفوذ آب، زه‌کشی مناسب، و کاشت پوشش گیاهی متناسب با محیط در دامنههای آماده‌ی لغزش ممکن است برای جلوگیری از این رویداد و مهار آن مناسب باشد. | ||
| کلیدواژه‌ها | ||
| تابع شاهد قطعی؛ سامانه‌ی اطلاعات جغرافیایی؛ شبکه‌ی عصبی پیچشی؛ شبیه سازی؛ ماشین بردار پشتیبان | ||
| عنوان مقاله [English] | ||
| Landslide Susceptibility Simulation Using Data Mining Models in Rabor Area | ||
| نویسندگان [English] | ||
| Elham Rafiei Sardooi | ||
| Associate Professor, Department of Ecological Engineering, Faculty of Natural Resources, University of Jiroft | ||
| چکیده [English] | ||
| Landslide, as one of the most important natural hazards, causes financial losses and destruction of natural resources, every year. Rabar area, located upstream of Halilrood watershed, is prone to landslides due to the presence of marl formations and hence, a high amount of sediment has entered the Safa reservoir in Rabar city. Therefore, the purpose of this study is to zone this environmental hazard using convolutional neural network (CNN), support vector machine (SVM) and evidential belief function (EBF) models in Rabor region. To achieve this purpose, the parameters of altitude, slope, and distance from the fault, geology, land use, soil type, Normalized Difference Vegetation Index, and distance from the river were used. Then, using the data of the Geological Survey of Iran and field observations using GPS, a landslide distribution map was prepared as a dependent variable. There were 70 landslides, 49 (70%) of which were used for simulating and 21 (30%) for model validation. The results obtained from the validation of the models using the ROC showed that the AUC values for CNN, SVM and EBF models were 0.987, 0.958 and 0.899, respectively. Overall, the results showed a satisfactory correlation between the landslide data available in the area and the landslide susceptibility maps and the deep learning model of the convolutional neural network had a higher performance compared with the other two models. Finally, the landslide susceptibility map was classified into four classes: low, medium, high, and very high susceptibility. According to the results of all models; the central, southeastern, and southwestern parts of the study area have a high and very high landslide risk. Carrying out appropriate designs such as retaining walls, preventing water infiltration, appropriate drainage, planting vegetation suitable for the environment, and landslide-prone slopes and etc can be appropriate in preventing and controlling this hazard. | ||
| کلیدواژه‌ها [English] | ||
| Convolutional neural network, evidence belief function, geographic information system, simulation, support vector machine | ||
| مراجع | ||
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