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مدلسازی حساسیت به زمین‌لغزش با استفاده از شبکه‌های عصبی مصنوعی و رگرسیون لجستیک در حوضه سقزچای جنوب استان اردبیل | ||
| مهندسی و مدیریت آبخیز | ||
| مقاله 10، دوره 15، شماره 3، مهر 1402، صفحه 481-503 اصل مقاله (2.18 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijwmse.2022.360475.1996 | ||
| نویسندگان | ||
| رضا طلائی* 1؛ صمد شادفر2 | ||
| 1استادیار، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اردبیل (مغان)، سازمان تحقیقات، آموزش و ترویج کشاورزی، اردبیل، ایران | ||
| 2دانشیار، پژوهشکده حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران | ||
| چکیده | ||
| مقدمه زمین‌لغزش‌ها، یکی از مخاطرات طبیعی در مناطق کوهستانی هستند که ایمنی ساکنان و محیط زیست را تهدید می‌کنند. در چند دهه گذشته، زمین‌لغزش‌ها در حوضه سقزچی در جنوب استان اردبیل، باعث وارد شدن خسارت‌هایی به منابع طبیعی و انسانی شده‌اند. زمین‌لغزش‌ها، در بیش از 9.2 درصد (2600 هکتار) از مساحت این حوضه وجود دارند. در این حوضه نیز مانند سایر مناطق زمین‌لغزشی، برای برنامه‌ریزی و مدیریت اراضی نیاز به تحلیل کل منطقه است، تا بر اساس آن احتمال وقوع زمین‌لغزش در آینده برآورد شود. حل این موضوع با تحلیل توام ویژگی‌های ژئومورفولوژی، توپوگرافی، زمین‌شناسی، کاربری اراضی، هیدرولوژی و آب و هوایی حوضه در قالب لایه‌های اطلاعاتی در محیط سامانه‌های اطلاعات جغرافیایی در یک مقیاس منطقه‌ای، امکان‌پذیر است. ارزیابی حساسیت به زمین‌لغزش در حوضه سقزچای تا به حال با روش‌های نوین و با دقت بالا انجام نگرفته است. دقت و اعتبار هر مدل، با استفاده از روش منحنی ROC و بر مبنای سطح زیر آن (AUC) تعیین شد. نتایج به دست آمده از این پژوهش، می‌تواند در پیشبینی وقوع احتمالی زمین‌لغزش و کاهش خسارت در حوضه سقزچای مورد استفاده قرار گیرد. مواد و روش‌ها حوضه مورد پژوهش، با مساحت 27918 هکتار در جنوب استان اردبیل و در جنوب غرب شهرستان خلخال واقع شده است. در این حوضه، نقشه پراکنش 113 زمین‌لغزش تهیه شد که در آن به‌ترتیب 70 و30 درصد از زمین‌لغزش‌ها به داده‌های آموزشی و ارزیابی اختصاص داده شدند. ده عامل موثر، در وقوع زمین‌لغزش‌ها شامل درصد شیب، جهات شیب، فاصله از گسل‌ها، فاصله از رودخانه‌ها، فاصله از راه‌ها، فاصله از مناطق مسکونی، واحدهای سنگ‌شناسی، بیشینه شتاب افقی زمین، کاربری اراضی و مجموع بارندگی سالانه، در تحلیل مدل‌ها مورد استفاده قرار گرفتند. به‌منظور پیشبینی حساسیت به وقوع زمین‌لغزش، از دو روش غیرخطی شبکه عصبی به نام پرسپترون چند لایه با ساختار رو به جلو و رگرسیون لجستیک، استفاده شد. بر اساس هر دو مدل، احتمال وقوع زمین‌لغزش در هر پیکسل محاسبه شد. دقت پیشبینی دو مدل با استفاده از منحنی ROC، مورد ارزیابی قرار گرفت. نتایج و بحث در مدل شبکه‌های عصبی، عوامل تشدید کننده شامل میانگین بارندگی سالانه (0.136) و بیشینه شتاب افقی زمین (0.134)، بیشترین تاثیر را در پیشبینی احتمال وقوع زمین‌لغزش‌ها داشته‌اند. عوامل فاصله از گسل‌ها (0.110)، واحدهای سنگ‌شناسی (0.109)، فاصله از راه‌ها (0.109)، فاصله از رودخانه‌ها (0.101)، فاصله از مناطق مسکونی (0.096)، جهات جغرافیایی دامنه‌ها (0.069)، کاربری اراضی (0.068) و درصد شیب دامنه‌ها (0.067) به‌ترتیب در مدلسازی حساسیت به زمین‌لغزش به روش شبکه‌های عصبی مصنوعی اهمیت دارند. بنابراین، تمامی ده عامل در مدلسازی به روش شبکه‌های عصبی مصنوعی، به‌کار گرفته شدند. نتایج به دست آمده نشان داد که احتمال وقوع زمین‌لغزش در فاصله 0.00 تا 0.961، تغییر می‌نماید. در طبقهبندی حوضه به درجات حساسیت به زمین‌لغزش، به روش شکست طبیعی بر مبنای احتمال برآوردی روش شبکه‌های عصبی، 85.7 درصد از منطقه در پهنه‌های با حساسیت کم و بسیار کم، قرار می‌گیرد. در 6.6 درصد از منطقه، احتمال حساسیت به زمین‌لغزش متوسط و در 7.7 درصد از حوضه حساسیت بالا و بسیار بالا برای وقوع زمین‌لغزش وجود دارد. تحلیل حساسیت به زمین‌لغزش به روش رگرسیون لجستیک، با روش بدون متغیر مستقل شروع شد و با اضافه کردن متغیرها در قدم دهم، خاتمه یافت. نتایج نشان می‌دهد که تنها سه سطح از عامل جهات جغرافیایی، در مدل رگرسیون لجستیک بی‌‌‌اثر هستند. با تخمین ثابت و ضرایب مربوط به متغیرهای مستقل در تحلیل رگرسیون لجستیک، مقادیر احتمال بین صفر تا یک، برای تمام پیکسل‌های منطقه محاسبه شد. با درجه‌بندی حساسیت به زمین‌لغزش به روش شکست طبیعی در مدل رگرسیون لجستیک، به‌ترتیب 79.9، 10.1 و 10 درصد از مساحت منطقه در گروه با درجات حساسیت پایین و بسیار پایین، متوسط و بالا و بسیار بالا قرار می‌گیرد. دقت و اعتبار مدل‌های رگرسیون لجستیک و شبکه عصبی مصنوعی، بر اساس منحنی ROC و سطح زیر آن (AUC) به‌ترتیب برابر 0.848 و 0.929 است. نتایج هر دو مدل، خوب بوده است و دقت بالاتر از 84 درصد داشته‌اند. نتایج به دست آمده از دو روش فوق، در اکثر مطالعات در دنیا و ایران حکایت از توانمندی آن‌ها در برآورد دقیق حساسیت احتمالی به زمین‌لغزش‌ها دارد، اما روش شبکه‌های عصبی مصنوعی، با وجود پیچیدگی‌های خاص دارای دقت بیشتری است. نتیجه‌گیری زمین‌لغزش، یک محدودیت مهم برای توسعه در مناطق لغزش‌خیز جنوب استان اردبیل است. شرایط محیطی، در حوضه سقزچی برای وقوع زمین‌لغزش‌های جدید و یا فعالیت زمین‌لغزش‌های قدیمی مستعد است. احتمال وقوع زمین‌لغزش در منطقه با استفاده از عوامل موثر و به روش رگرسیون لجستیک و شبکه عصبی مصنوعی، شبیه‌سازی شد. نتایج حاصل از مدل شبکه عصبی مصنوعی، دقیق‌تر بوده و بهتر از مدل رگرسیون لجستیک است. در مدل شبکه‌های عصبی مصنوعی، عوامل تشدید کننده زمین‌لغزش‌ها شامل میانگین بارندگی سالانه و بیشینه شتاب افقی زمین، بیشترین تاثیر را بر چگونگی پیشبینی احتمال وقوع زمین‌لغزش‌ها دارند. روش شبکه عصبی مصنوعی، در تبیین رابطه وقوع زمین‌لغزش با عوامل موثر، برتری نشان داد. نقشه حساسیت خروجی از این مدل، به پنج طبقه حساسیت بسیار کم (71.4 درصد)، کم (14.3 درصد)، متوسط (6.6 درصد)، زیاد (4.3 درصد) و بسیار زیاد (3.4)، تقسیم شد. استفاده از مدل‌های شبکه عصبی مصنوعی، در ارزیابی حساسیت به زمین‌لغزش در حوضه و مناطق مشابه، به‌منظور کمک به تصمیم‌گیران، برنامه‌ریزان، مدیران کاربری اراضی و سازمان‌های دولتی در کاهش خطرات و آسیب‌ها، توصیه می‌شود. | ||
| کلیدواژه‌ها | ||
| احتمال؛ ارزیابی؛ پیشبینی؛ خسارت؛ دقت؛ عوامل موثر و مخاطرات طبیعی | ||
| عنوان مقاله [English] | ||
| Landslide susceptibility modeling using artificial neural network and logistic regression methods at the Saqezchay Basin, south of Ardabil Province | ||
| نویسندگان [English] | ||
| Reza Talaei1؛ Samad Shadfar2 | ||
| 1Assistant Professor, Soil Conservation and Watershed Management Research Department, Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ardabil, Iran | ||
| 2Associate Professor, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran | ||
| چکیده [English] | ||
| Introduction Landslides are one of the natural hazards in mountainous areas that threaten the safety of residents and the environment. In the past few decades, landslides have caused damage to natural and human resources in the Saqezchi Basin in the south of Ardabil Province. Landslides have occurred in more than 9.2% (2600 ha) of the area. In this basin, like other landslide areas, for land-use planning and management, it is necessary to analyze the whole area in order to estimate the probability of landslides occurrence in the future. It is possible to solve this problem by analyzing the geomorphology, topography, geology, land use, hydrology and climate factors of the basin in the form of information layers in the geographic information systems on a regional scale. Landslide susceptibility assessment has not been done with modern methods and with high accuracy in the Saqzachai Basin until now. The results of this research can be used in predicting the possible occurrence of landslides and reducing damage in the Saqzachai Basin. Materials and methods The research basin with an area of 27,918 ha is located in the south of Ardabil Province and in the southwest of Khalkhal City. In this basin, the inventory map was generated based on 113 landslides, the training dataset and validation dataset were, respectively, prepared using 70% landslides and the remaining 30% landslides. Ten landslide causative factors based on slope angle, slope aspect, distance to faults, distance to stream network, distance to the roads, distance to settlement area, lithology, land-use, peak ground acceleration (PGA) and average annual precipitation were applied for the models analysis. Two nonlinear methods of neural network called multi-layer perceptron with feed forward structure and logistic regression were used to predicting the susceptibility of landslide occurrence. The probability of landslide occurrence in each pixel was calculated based on both models. The prediction accuracy of the two models were evaluated using the Receiver Operating Characteristic (ROC) curve. Results and discussion In the neural network model, landslides triggering factors, including the average annual precipitation (0.136) and the peak ground acceleration (0.134), have been the greatest effect in predicting the probability of landslides. The factors of slope angle (0.067), slope aspect (0.069), distance to faults (0.110), distance to stream network (0.101), distance to the roads (0.109), distance to settlement area (0.096), lithology (0.109) and land-use (0.068) are respectively important in landslides susceptibility modeling to using artificial neural networks. Therefore, all ten factors were used in modeling by artificial neural networks. The results indicated that the probability of landslide occurrence varies from 0.00 to 0.961. In the classification of the watershed according to the degree of landslide susceptibility by the natural breaks method based on the estimated probability by the neural network method, 85.7% of the area is placed in the zones with low and very low susceptibility. In 6.6% of the area, there is a probability of moderate susceptibility, and in 7.7%, there is a high and very high landslide susceptibility. Landslide susceptibility analysis is started without independent variable and ended by adding variables in the tenth step using logistic regression method. The results show that only three levels of the factor of slope aspect are ineffective in the logistic regression model. Probability values were calculated between 0 and 1 for all pixels in the area based on the values of independent variables by estimating constant and coefficients related to logistic regression model. The landslide-prone areas of low and very low susceptibility, medium susceptibility and high to extremely high-susceptibility grades are 79.9%, 10.1%, and 10%, of area, respectively, by the natural breaks method in the logistic regression model. The accuracy and validity of the logistic regression and artificial neural network models based on the ROC curve and the area under it (AUC) are equal to 0.848 and 0.929, respectively. The findings of the models show good results with the accuracy of two models being higher than 84%. The results obtained from two methods in most studies in the world and in Iran indicate their ability to accurately estimate susceptibility to landslides occurrence, but the artificial neural network method is more accurate despite its specific complexities. Conclusion Landslides are an important limitation for development in the landslide areas in the south of Ardabil Province. The environmental conditions in the Saqzachi Basin are susceptible to the occurrence of new landslides or the reactivation of old landslides. The probability of landslides occurrence was simulated using effective factors and using logistic regression and artificial neural network models in the region. The results obtained from the artificial neural network model are the most accurate and better than the logistic regression model. The landslides triggering factors, including the average annual precipitation and the peak ground acceleration have the greatest impact to predicting the probability of landslide occurrence using the artificial neural network model. The findings of the models show good results with the accuracy of two models being higher than 84%. The artificial neural network method is superior in explaining the relationship between landslide occurrence and influencing factors. The landslide susceptibility map was prepared using this method by dividing into five class, namely: very low (71.4%), low (14.3%), moderate (6.6%), high (4.3%) and very high (3.4%) susceptibility zones. Therefore, it is recommended to use the artificial neural network models in landslide susceptibility assessment in the basin and similar regions to help decision makers, planners, land use managers and government agencies in hazard and damage reduction. | ||
| کلیدواژه‌ها [English] | ||
| Accuracy, Assessment, Damage, Effective factors, Natural hazards, Prediction and probability | ||
| مراجع | ||
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