خلاصة:
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.
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.
ملخص الجهاز:
به عنوان مثال ؛ شيرزادي و همکاران )١٣٩٦( به ارائه يک مدل جديد ترکيبي الگوريتم مبنا به منظ ور پيش بيني حساسيت زمين لغزش هاي سط حي اط راف شهر بيجار پرداختند نتايج ارزيابي صحت نقشه پهنه بندي به دست آمده نشان داد که درصد مساحت زير منحنيAUROC) ROC( براي داده هاي تعليمي در مدل RF و مدل ترکيبي RF-RS به ترتيب ٠/٧٢٩ و ٠/٧٨٤ و براي مدل ترکيبي جديد ارائه شده به ترتيب ٠/٧١٧ و ٠/٧٧١ به دست آمدند.
مدل ترکيبي (RFFT)Functional Tree -Rotation Forest مدل ترکيبي RFFT يک رويکرد ترکيبي از گروه FT و ط بقه بندي FT است که اساس کار در سه مرحله به صورت زير ميباشد: مرحله اول ، بهينه سازي: در اين مرحله مجموعه گروه RF جهت بهينه سازي داده هاي ورودي براي مدل سازي استفاده ميشود.