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

زمین لغزش ها به عنوان یکی از مخرب ترین پدیده های طبیعی محسوب می شوند. به دلیل تهدید آن ها، باید یک نقشه جامع حساسیت زمین لغزش برای کاهش آسیب های احتمالی به افراد و زیرساخت ها تهیه شود. کیفیت نقشه های حساسیت زمین لغزش تحت تاثیر بسیاری از عوامل، از جمله کیفیت داده های ورودی و انتخاب مدل های ریاضی است. هدف اصلی این پژوهش ارائه یک مدل ترکیبی جدید داده کاوی به نام rotation forest functional trees (rfft) که یک رویکرد هوشمند ترکیبی از دو تکنیک یادگیری ماشین مدل functional trees (ft) و تکنیک طبقه بندی مدل rotation forest (rf) برای ارزیابی حساسیت زمین لغزش های اطراف شهر کامیاران واقع در استان کردستان می باشد. در ابتدا، بیست و یک عامل موثر بر وقوع زمین لغزش های 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 زمین لغزش با 60 نقطه لغزشی برای جمع آوری داده های آموزشی و آزمون جمع آوری شدند. سپس، بر اساس شاخص information gain ratio هفده عامل موثر از بین آن ها انتخاب و جهت مدل سازی به کار گرفته شدند. در مرحله بعد  مدل هیبریدی rfft برای ارزیابی حساسیت زمین لغزش با استفاده از مجموعه داده های 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.

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

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, twentyone 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 lengthangle 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 statisticalbased 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, twentyone 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 lengthangle 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 statisticalbased 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, twentyone 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 lengthangle 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 statisticalbased 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.