ارزیابی مکانی و پهنه‌بندی خطر زمین‌لغزش حوضه آبریز زمکان با استفاده از ماشین بردار پشتیبان و رگرسیون لجستیک

اسفندیاری, فریبا; رستمی, قباد; مصطفی‌زاده, رئوف; عابدینی, موسی

doi:10.22034/hyd.2024.61467.1737

نوع مقاله : پژوهشی

نویسندگان

¹ استاد گروه جغرافیای طبیعی، دانشکده‌ علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران

² دانشجوی دکتری ژئومورفولوژی، دانشکده‌ علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران

³ دانشیار گروه منابع طبیعی و عضو پژوهشکده مدیریت آب، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی

10.22034/hyd.2024.61467.1737

چکیده

در پژوهش حاضر خطر وقوع زمین‌لغزش در حوضه آبریز زمکان، واقع در استان کرمانشاه، ارزیابی شد. دو مدل ماشین‌بردار پشتیبان (SVM) و رگرسیون لجستیک برای تهیه نقشه حساسیت زمین‌لغزش استفاده شد. در راستای اهداف تحقیق، 13 لایه اطلاعاتی شامل ارتفاع، شیب، جهت شیب، عدد ناهمواری ملتون، تحدب سطح زمین، طول دامنه، عمق دره، رطوبت توپوگرافیک، بارش، سازندهای زمین‌شناسی، فاصله از آبراهه، فاصله از جاده و پوشش گیاهی به‌عنوان متغیرهای مستقل استفاده شد. حدود 70 درصد پیکسل‌های لغزشی حوضه به‌منظور آموزش و 30 درصد برای اعتبارسنجی مدل استفاده شدند. اعتبارسنجی مدل‌ها با کاربست منحنی ROC صورت گرفت. نتایج نشان‌دهنده کارایی و دقت بالاتر تابع پایه شعاعی (RBF) مدل SVM برای تهیه نقشه خطر زمین‌لغزش منطقه است. مساحت زیر منحنی (AUC) تابع پایه شعاعی حدود 951/0 برای آموزش مدل و 944/0 برای آزمون مدل به‌دست آمد. نتایج بیانگر این است که فاکتورهای شیب با ضریب 28/0، بارش با ضریب 27/0، لیتولوژی با ضریب 26/0 و ارتفاع با ضریب 22/0 کنترل‌کننده‌های اصلی وقوع زمین‌لغزش در سطح حوضه آبریز زمکان هستند. توابع مدل SVM و هم‌چنین رگرسیون لجستیک نیز اثرات قطعی فاکتورهای انتخابی بر وقوع زمین‌لغزش را تائید کردند. براساس نقشه پهنه‌بندی زمین‌لغزش حدود 35 درصد مساحت حوضه مطالعاتی در کلاس خطرپذیری زیاد و بسیار زیاد قرار گرفته است. پهنه‌های مذکور عمدتاً در نیمه شرقی حوضه توزیع شده‌اند. ارتفاع زیاد، غلبه شیب‌های تند، دریافت نزولات جوی قابل توجه و رخنمون وسیع سازند کژدمی با تناوبی از لایه‌های آهکی، رسی، مارنی و شیلی مهم‌ترین دلایل حساسیت بالای این پهنه‌ها نسبت به زمین‌لغزش هستند.

کلیدواژه‌ها

موضوعات

ژئومورفولوژی

عنوان مقاله [English]

Spatial assessment and zoning of landslide risk in Zamkan watershed using support vector machine and logistic regression

نویسندگان [English]

Fariba Esfandyari Darabad ¹
Ghobad Rostami ²
Raoof Mostafazadeh ³
Mousa Abedini ¹

¹ Professor, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

² Ph.D student, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

³ Associate Professor, Department of Natural Resources and member of Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili

چکیده [English]

In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including elevation, slope, aspect, Melton ruggedness number, terrain convexity, stream length, valley depth, topographic wetness index, precipitation, geological formations, distance from rivers, distance from roads, and vegetation cover were utilized as independent variables. Approximately 70% of the watershed's landslide pixels were used for model training, and 30% for model validation. Model validation was performed using ROC curves. The results indicated the higher performance and accuracy of the radial basis function (RBF) kernel of the SVM model for generating landslide hazard maps in the study area. The area under the curve (AUC) for the RBF kernel was approximately 0.951 for model training and 0.944 for model testing. The results suggest that slope with a coefficient of 0.28, precipitation with a coefficient of 0.27, lithology with a coefficient of 0.26, and elevation with a coefficient of 0.22 are the main controlling factors for landslides occurrence in the Zamkan Watershed. Both the SVM model and logistic regression confirmed the deterministic effects of selected factors on landslides. About 35% of the study area as classified as highly susceptible to landslides, primarily in the eastern half of the watershed. Factors such as high elevation, steep slopes, heavy precipitation, and the Kazhdomi Formation's composition were identified as key contributors to this susceptibility.

کلیدواژه‌ها [English]

Landslide
Logistic regression
Support Vector Machine (SVM)
Zamkan watershed
Kermanshah province

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هیدروژئومورفولوژی

ارزیابی مکانی و پهنه‌بندی خطر زمین‌لغزش حوضه آبریز زمکان با استفاده از ماشین بردار پشتیبان و رگرسیون لجستیک

مراجع

مراجع

دوره 11، شماره 40 - شماره پیاپی 40
مهر 1403
صفحه 123-102

ارزیابی مکانی و پهنه‌بندی خطر زمین‌لغزش حوضه آبریز زمکان با استفاده از ماشین بردار پشتیبان و رگرسیون لجستیک

مراجع

مراجع

دوره 11، شماره 40 - شماره پیاپی 40مهر 1403صفحه 123-102

دوره 11، شماره 40 - شماره پیاپی 40
مهر 1403
صفحه 123-102