جغرافیا و مخاطرات محیطی

جغرافیا و مخاطرات محیطی

ارزیابی و مقایسه کارایی مدل‌های یادگیری ماشین ‌SVM، ANN و MaxEnt در تهیه نقشه حساسیت زمین‌لغزش آبخیز زیارت، استان گلستان

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی، گرگان، ایران
2 مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان، گرگان، ایران
چکیده
زمین‌لغزش یکی از خسارت‌بارترین مخاطرات دامنه‌ای در مناطق کوهستانی است. کارایی سه مدل یادگیری ماشین شامل شبکه عصبی مصنوعی (ANN)، ماشین بردار پشتیبان (SVM) و بیشینه آنتروپی (MaxEnt) در تهیه نقشه حساسیت زمین‌لغزش آبخیز زیارت (استان گلستان) ارزیابی و مقایسه شد. با برداشت میدانی GPS، ۱۱۶ نقطه لغزشی ثبت شد. به منظور حفظ پراکنش فضایی نقاط در واحدهای زمین‌شناسی، طبقات ارتفاعی و شیب، از روش نمونه‌گیری تصادفی طبقه‌ای استفاده شد. ۸۱ نقطه (۷۰درصد) برای آموزش و ۳۵ نقطه (۳۰درصد) برای اعتبارسنجی تخصیص یافت. در مجموع ۵ گروه عامل اصلی (توپوگرافی، هیدرولوژی، زمین‌شناسی، مورفومتری و انسانی) با ۲۳ زیرعامل (شیب، جهت، ارتفاع، عمق دره، انحنای پروفیل و سطح، شاخص توپوگرافی، موقعیت نسبی شیب، همگرایی، بازشدگی مثبت و منفی، بارندگی، فاصله از آبراهه، شاخص رطوبت توپوگرافی، شاخص توان آبراهه، جریان تجمعی، سازند زمین‌شناسی، فاصله از گسل، بافت خاک، شاخص تعادل جرم، شاخص زبری ملتون، شاخص قدرت بردار) انتخاب شدند. مدل‌سازی با نرم‌افزار ModEco و ارزیابی دقت با منحنی ROC و شاخص سطح زیر منحنی (AUC) انجام شد. یافته‌ها نشان داد که مدل ANN با AUC معادل 0.936 و مدل SVM با AUC معادل 0.934 عملکردی خوب و بسیار نزدیک دارند و مدل MaxEnt با AUC معادل 0.904 در رتبه پایین‌تری قرار می‌گیرد. مؤثرترین عوامل وقوع زمین‌لغزش به ترتیب ارتفاع (منطبق بر پهنه بارش بالای ۵۵۰ میلی‌متر)، سازندهای شمشک و شیست گرگان، و فاصله از مناطق مسکونی بودند. با توجه به تغییرات کاربری اراضی و افزایش ساخت‌وساز در آبخیز زیارت، اعمال محدودیت در تغییر کاربری و ممنوعیت ساخت‌وساز در دامنه‌های شیب ‌تند پیشنهاد می‌شود.
کلیدواژه‌ها
موضوعات

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