ارزیابی حساسیت زمین‌لغزش با استفاده از مدل جدید ترکیبی الگوریتم مبنا (مطالعه موردی: شهرستان کامیاران، استان کردستان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته دکترای ژئومورفولوژی از گروه جغرافیای طبیعی ،دانشگاه محقق اردبیلی، اردبیل، ایران.

2 استاد گروه جغرافیای طبیعی (ژئومورفولوژی)، دانشگاه محقق اردبیلی، اردبیل، ایران.

3 استاد گروه جغرافیای طبیعی (ژئومورفولوژی)، دانشگاه تبریز، تبریز، ایران.

4 کارشناس آموزشی و پژوهشی گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه کردستان

10.22034/gmpj.2021.131015

چکیده

زمین­لغزش­ها به عنوان یکی از مخرب­ترین پدیده­های طبیعی محسوب می­شوند. به دلیل تهدید آن­ها، باید یک نقشه جامع حساسیت زمین­لغزش برای کاهش آسیب­های احتمالی به افراد و زیرساخت­ها تهیه شود. کیفیت نقشه­های حساسیت زمین­لغزش تحت تأثیر بسیاری از عوامل، از جمله کیفیت داده­های ورودی و انتخاب مدل­های ریاضی است. هدف اصلی این پژوهش ارائه یک مدل ترکیبی جدید داده­کاوی به نام Rotation Forest - Functional Trees (RF-FT) که یک رویکرد هوشمند ترکیبی از دو تکنیک یادگیری ماشین مدل Functional Trees (FT) و تکنیک طبقه­بندی مدل Rotation Forest (RF) برای ارزیابی حساسیت زمین لغزش­های اطراف شهر کامیاران واقع در استان کردستان می­باشد. در ابتدا، بیست و یک عامل مؤثر بر وقوع زمین­لغزش­های منطقه مورد مطالعه شامل درجه شیب، جهت شیب، ارتفاع، انحنای شیب، انحنای عرضی شیب، انحنای طولی شیب، تابش خورشید، عمق دره، شاخص قدرت جریان، شاخص نمناکی توپوگرافی، شاخص طول دامنه، کاربری اراضی، تراکم پوشش گیاهی، فاصله از گسل، تراکم گسل، فاصله از جاده، تراکم جاده، فاصله از آبراهه، تراکم آبراهه، همباران و لیتولوژی  به همراه نقشه پراکنش زمین­لغزش با 60 نقطه لغزشی برای جمع­آوری داده­های آموزشی و آزمون جمع­آوری شدند. سپس، بر اساس شاخص Information Gain Ratio هفده عامل مؤثر از بین آن­ها انتخاب و جهت مدل­سازی به کار گرفته شدند. در مرحله بعد  مدل هیبریدی RFFT برای ارزیابی حساسیت زمین­لغزش با استفاده از مجموعه داده­های آموزشی ساخته شد. عملکرد مدل پیشنهادی RFFT با استفاده از چندین پارامتر آماری از جمله حساسیت، شفافیت، صحت، مجذور مربعات خطا، منحنی نرخ موفقیت و سطح زیر این منحنی مورد ارزیابی قرار گرفت.
 

کلیدواژه‌ها


عنوان مقاله [English]

Landslide susceptibility assessment using a novel ensemble algorithm based model (Case Study: Kamyaran city, Kurdistan province)

نویسندگان [English]

  • Bahareh Gasemyan 1
  • Mousa Abedini 2
  • Shahram Roostai 3
  • Ataalah Shirzadi 4
1 mohaghegh ardabili university
2 professor in Geomorphology, University of Mohaghegh Ardabili
3 Department of Physical Geography, University of Tabriz
4 Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
چکیده [English]

Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.Landslides are considered one of the most destructive natural phenomena. Landslides are dangerous natural hazards. Because of their threat, a comprehensive landslide susceptibility map should be produced to reduce the possible damages to people and infrastructure. The quality of landslide susceptibility maps is influenced by many factors, such as the quality of input data and the selection of mathematical models. The main purpose of this study is to presentation, a novel hybrid model namely Rotation Forest based Functional Trees (RFFT), which is a hybrid intelligent approach of two state of the art machine learning techniques of Functional Trees (FT) classifier and Rotation Forest (RF) ensemble, for landslide susceptibility Assessment prediction in Kamyaran city located in Kurdistan Province, Iran. At first, twenty-one factors affecting the occurrence of landslide in the study area including Slope angle, Aspect, Elevation, Curvature, Plan curvature, Profile curvature, Radiation, Valle depth(VD), stream power index (SPI), topographic wetness index (TWI), combination of length-angle of slope (LS), Land use, NDVI (normalized vegetation index), Distance to Faults, Faults density, Distance to Road, Road density, Distance to River, River density Lithology and Rainfall with total of 60 landslide locations have been collected for generating training and testing datasets. Then, based on the Information Gain Ratio Index, eight effective factors were chosen and used for modeling. Performance of the proposed RFFT model was evaluated using some statistical-based measures such as sensitivity, specificity, accuracy, RMSE and area under the ROC curve (AUROC). The results showed that the proposed model performed well in this study (AUC = 0.891), and it improved significantly the performance of the FT base classifier (AUC = 0.819). Therefore, it can be concluded that the proposed RFFT model should be used as a great alternative method for better landslide susceptibility assessment in landslide prone area.

کلیدواژه‌ها [English]

  • Landslide
  • Hybrid Model
  • Information Gain Ratio Index
  • Kamyaran