نشریه محیط زیست طبیعی

ارزیابی نقش ویژگی های ژئومورفیک مؤثر بر آسیبپذیری و حساسیت اراضی به زمین لغزش با استفاده از روش های یادگیری ماشین (مطالعة حوزة آبخیز کاکاشرف استان لرستان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه آبخیزداری، دانشکدة منابع طبیعی، دانشگاه تهران. تهران، کرج، ایران.

2 گروه احیا مناطق خشک و کوهستانی، دانشکدة منابع طبیعی، دانشگاه تهران، کرج. ایران.

3 ادارة مهندسی و مطالعات ادارة کل منابع طبیعی و آبخیزداری لرستان، لرستان، ایران.

10.22059/jne.2024.376982.2675

چکیده

زمین‌لغزش‌ها به‌عنوان یک مخاطره و اختلال زمینی، اثرات مستقیم و غیر مستقیمی زیادی بر شرایط جوامع انسانی، محیط ‌طبیعی و تغییر سیمای سرزمین دارند و می‌توانند موجب خسارت­های جانی و مالی گسترده‌ای شود. بنابراین تعیین عوامل مؤثر بر آسیب­پذیری به آن برای مدیریت این مخاطره ضروری است. هدف این پژوهش، شناخت عوامل مؤثر بر حساسیت اراضی به زمین‌لغزش و تهیة نقشة حساسیت به زمین‌لغزش با پایۀ تلفیق روش ­های یادگیری ماشین و مدل­سازی آماری است. در این بررسی با استفاده از الگوریتم جنگل تصادفی، از بین عوامل مؤثر (شیب، جهت شیب، موقعیت توپوگرافی، رطوبت توپوگرافی، انحنای سطحی، سنگ ­شناسی، فاصله از گسل، فاصله ازآبراهه، فاصله از جاده و کاربری اراضی) وزن و درجۀ  اهمیت مشخص شد. سپس با تکنیک­ های شبکة عصبی مصنوعی و MaxEnt برای مدل‌سازی و پیش‌بینی مناطق مستعد زمین‌لغزش استفاده شد. برای ارزیابی دقت مدل ­ها، با انجام اعتبار‌سنجی در محدودۀ مجاور حوزة آبخیز کاکاشرف، شاخص‌های ارزیابی محاسبه شد. نتایج نشان داد که عوامل فاصله از گسل و شیب، به‌ترتیب بیشترین اهمیت را در حساسیت اراضی به زمین‌لغزش دارند. بر پایة شاخص سطح زیر منحنی (AUC) مدل شبکة عصبی 0/92 نسبت به مدل  MaxEnt با مقدار 0/801 دقت بیشتری در پیش‌بینی مناطق مستعد به زمین‌لغزش را نشان داد. بیش‌ترین آسیب‌پذیری متعلق به اراضی مجاور آبراهه­ فاصلة کمتر از 200 متر با خطوط گسلش و شیب 40-20% است؛ بنابراین مدیریت و پوشش کاربری اراضی در این اراضی از اولویت بیشتری برخوردار است. این نتایج می‌تواند در بهبود مدیریت و برنامه‌ریزی اراضی در مناطق مستعد زمین‌لغزش، حفاظت از منابع‌طبیعی و مدیریت ریسک مؤثر باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluating the role of geomorphic characteristics in landslide vulnerability and sensitivity (Case study: Kakasharaf Drainage Basin, Lorestan Province)

نویسندگان [English]

  • Reza Fathiganji 1
  • Aliakbar Nazari Samani 2
  • Sadat Feiznia 2
  • Abdolreza Nourizdan 3

1 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

2 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

3 Department of Engineering and Studies of the General Department of Natural Resources and Watershed of Lorestan, Iran.

چکیده [English]

Landslides, as a natural hazard and geomorphic disturbance, have numerous direct and indirect impacts on human communities, the natural environment, and landscape transformation, and can lead to significant human and financial losses.Therefore, identifying the factors influencing vulnerability to this hazard is essential for effective management. The aim of this study is to identify the factors affecting land susceptibility to landslides and to prepare a susceptibility map by integrating machine learning methods and statistical modeling. In this research, the Random Forest algorithm was used to determine the weight and importance of influencing factors including slope, aspect, topographic position index, topographic wetness index, Plan curvature, lithology, distance from faults, distance from stream, distance from roads, and land use. Subsequently, Artificial Neural Networks (ANN) and Maximum Entropy (MaxEnt) models were applied to model and predict landslide-prone areas. To evaluate model performance, validation was conducted in an adjacent area to the Kakashraf watershed, and relevant evaluation indices were calculated. The results indicated that distance from faults and slope were the most significant factors influencing landslide susceptibility. According to the Area Under the Curve (AUC), the ANN model (0.92) had higher predictive accuracy than the MaxEnt model (0.801). The most vulnerable areas were found within 200 meters of stream, near fault lines, and with slopes between 20% and 40%. Therefore, land use management in such areas should be prioritized. These findings can contribute to improved land planning, natural resource protection, and effective landslide risk management.

کلیدواژه‌ها [English]

  • MaxEnt
  • Neural network
  • Random forest
  • Sensitivity
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نشریه محیط زیست طبیعی
دوره 77، شماره 4
اسفند 1403
صفحه 731-747