ارزیابی کارایی مدل‌های یادگیری ماشین در تهیه نقشه خطر زمین‌لغزش در آبخیز بار نیشابور

نوع مقاله : پژوهشی

نویسندگان

1 استادیار بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان خراسان رضوی، سازمان تحقیقات، آموزش و ترویج کشاورزی، مشهد، ایران

2 استادیار بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان لرستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، خرم آباد، ایران

10.22092/wmrj.2023.361650.1531

چکیده

مقدمه و هدف
پهنه‌بندی حساسیت رخداد زمین‌لغزش با استفاده از روش‌های گوناگون، یکی از راهکارهای مدیریت زمین‌لغزش است. هدف از این پژوهش، مدل‌سازی مکانی حساسیت رخداد زمین‌لغزش با استفاده از سه روش مدل یادگیری ماشین جنگل تصادفی (RF)، بیشینه‌ی آنتروپی (ME) و مدل ماشین‌بردار پشتیبان (SVM) بود. افزون بر این، کارایی این مدل‌ها در پهنه‌بندی حساسیت رخداد زمین‌لغزش در آبخیز بار نیشابور، استان خراسان رضوی مقایسه شد.
مواد و روش‌ها
در این پژوهش، لایه‌ی نقشه‌ی پراکنش زمین‌لغزش‌های آبخیز بار با 73 نقطه‌ی ثبت‌شده، تهیه شد. این نقاط به‌شکل تصادفی به دو دسته برای آموزش مدل (70%) و اعتبارسنجی مدل (30%) تقسیم شدند. همچنین، با توجه به بررسی منابع گسترده، 16 عامل مؤثر بر رخداد زمین‌لغزش در منطقه‌ی مطالعه‌شده شناسایی شد و لایه‌های رقومی در سامانه‌ی اطلاعات جغرافیایی تهیه شد. سپس نقشه‌ی خطر (استعداد) زمین‌لغزش بر اساس سه روش مزبور تهیه شد. سرانجام، برای ارزیابی صحت مدل سازی و مقایسه‌ی کارایی مدل‌ها از شاخص جمع کیفیت (Qs) استفاده شد.
 نتایج و بحث
نتایج این پژوهش نشان داد که روش مدل جنگل تصادفی (RF) به‌عنوان مدل برتر (0/018 =Qs) برای آبخیز برگزیده شد. مدل‌های بردار پشتیبان (SVM) با Qs برابر با 0/014 و مدل بیشینه‌ی آنتروپی (ME) با Qs برابر با 0/013 به‌ترتیب اولویت‌های بعدی بودند.
نتیجه‌گیری و پیشنهادها
بر اساس نتایج این پژوهش مدل جنگل تصادفی هم نتایج بهتر و هم کاربردی‌تر ارائه داد. تطبیق نتایج به‌دست آمده از این مدل با شرایط واقعی موجود با بازدیدهای میدانی انجام شد. افزون بر این میان نتایج نقشه‌ی پهنه‌بندی حساسیت زمین‌لغزش با استفاده از مدل جنگل تصادفی و شرایط واقعی موجود در منطقه‌ی مطالعه‌شده تطبیق بسیار زیادی وجود داشت. سرانجام مشخص شد که با فرض تمرکز عملیات مدیریتی در طبقه‌‌های با حساسیت زیاد و انتخاب مدل جنگل تصادفی به‌عنوان مدل برتر، 75/5%  از مساحت منطقه از روند مدیریتی خارج‌شده است. بنابراین، برای مدیریت این بخش به زمان کم‌تر و تخصیص منابع مالی نیاز است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluating the Effectiveness of Machine Learning Models in Preparing a Landslide Risk Map in the Bar Neyshabur Watershed

نویسندگان [English]

  • Ali Dastranj 1
  • Ebrahim Karimi Sangchini 2
  • Hamzeh Noor 1
1 Assistant Professor, Soil Conservation and Watershed Management Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran
2 Assistant Professor, Soil Conservation and Watershed Management Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, AREEO, Khorramabad, Iran
چکیده [English]

Introduction and Goal
Landslide susceptibility zoning using different methods is one of the solutions for landslide management. The aim of the upcoming study is to model the sensitivity of landslide occurrence using three methods of machine learning algorithm, random forest (RF), maximum entropy (ME) and support vector machine (SVM) algorithm. Then, the efficiency of these models is compared in zoning the sensitivity of landslides in the Bar Neyshabur watershed, Razavi Khorasan province.
Materials and Methods
In this research, the landslide distribution map layer of Bar watershed with 73 recorded points was prepared. These points were randomly divided into two groups for model training (70%) and model validation (30%). Also, 16 factors affecting the occurrence of landslides in the studied area were identified according to the review of extensive sources and digital layers were prepared in the geographic information system. Then, the landslide hazard map was prepared based on the three mentioned methods. Next, in order to evaluate the accuracy of modeling and compare the efficiency of the models, the total quality index (Qs) was used.
Results and Discussion
The results showed that the random forest algorithm method (RF) with Qs = 0.018 was chosen as the best model for the basin. Support vector models (SVM) with Qs = 0.014 and maximum entropy (ME) model with Qs = 0.013 are in the next priority, respectively.
Conclusion and Suggestions 
Based on the results of this research, the random forest model provided better results. The comparison of the results obtained from this model with the existing real conditions was done with field visits. In addition, the results of the landslide susceptibility zoning map using the random forest model and the actual conditions in the studied area were very compatible. Finally, it was determined that assuming the concentration of management operations in high-sensitivity classes and choosing the random forest model as the superior model, 75.5% of the region's area has been left out of the management process. Therefore, less time and financial resources are needed to manage this sector.

کلیدواژه‌ها [English]

  • Bar watershed
  • Random Forest
  • Razavi Khorasan Province
  • Landslide

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  • تاریخ دریافت: 10 اسفند 1401
  • تاریخ بازنگری: 29 خرداد 1402
  • تاریخ پذیرش: 30 شهریور 1402

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