انجمن ایرانی ژئومورفولوژی پژوهشهای ژئومورفولوژی کمّی 22519424 7 2 2018 11 22 A Comparison of Artificial Neural Network Model With Fuzzy logic model In Landslide Hazard Assessment مقایسه مدل شبکه عصبی مصنوعی با مدل منطق فازی در ارزیابی خطر زمین‌لغزش(مطالعه موردی حوضه آبریز سیمره چنار) 158 182 81029 FA صیاد اصغری سراسکانرود دانشیار ژئومورفولوژی دانشگاه محقق اردبیلی ایمانعلی بلواسی دانشجوی دکتری ژئومورفولوژی دانشگاه محقق اردبیلی 0000-0001-7479-7161 Journal Article 2018 05 21 Introduction Landslide is considered as one of the natural hazards ever occurring throughout the world and is of great importance. This phenomenon is one of the major geomorphic processes affecting the evolutionary landscape in mountainous regions, which has caused catastrophic accidents. Due to the special climate conditions, physiography and change of the country, Iran has always faced with the problem of mass movements, and it is necessary to pay attention to this natural limitation. Lorestan province also has diverse geological features such as petrography, land management, seismicity and special climate conditions, including areas with slip potential. The topographic and geological conditions of the study area are such that the slip of rock and soil fragments from small to large scale has been provided. Methodology Fuzzy logic is a logic of several values, that is, its parameters and variables, in addition to the number of 0 or 1, can take all the values between these two numbers. The basis of the difference between fuzzy methods and other methods is to define the membership function. The membership function can be used to determine the degree of attribution of the elements of the reference set to its subset. The operator of the fuzzy society is the collection community.In this way, it extracts the maximum membership membership. The fuzzy subscription operator is the collection subscription. In that way, it extracts the minimum degree of membership. Fuzzy algebraic multiplication multiplies all the information layers together. Because of the nature of the numbers between zero and one, which is the same as the membership membership in a fuzzy set, the operator makes the number of the numbers smaller and goes down to zero. The complementary fuzzy algebra operator is obtained by the algebraic multiplication. Therefore, in the outbound map, unlike the fuzzy algebraic operator, the value of pixels goes toward one. The fuzzy gamma operator is the product of multiplication, fuzzy coherent multiplication in the fuzzy algebraic summation. The results obtained from this operator are more accurate than other operators.An artificial neural network is a computational mechanism that can provide a series of new information by gathering information and calculating them. In the artificial neural network, the structure of the human brain and the body's neural network is similar to that of a brain that has the power to learn, make and decide. In the neural network model for preparing the network from the layers, along with a number of real samples, they entered the network as inputs, and with this method a pattern was obtained between the input parameters and the areas where the landslide was located. A probability ratio was used to determine the land slide sensitivity index. In order to facilitate artificial neural network convergence, the values of the input neurons were normalized. To estimate the accuracy of artificial neural network, the mean squared error error was used. Results and discussion An artificial neural network with multilayer perceptron structure with error propagation algorithm and non-linear sigmoid function as an activation function was used. The simple learning factor was ignored because of convergence and failure to make a valid error. Also, the error rate of the variable learning coefficient was higher than the Levenberg-Marquard method, which is why the Levenberg-Marquard method was used. For training and network testing, 80% of the data was used for training and 20% for testing. The final structure of the 1-14-8 grid was considered appropriate and based on this structure, the final zoning was performed.The operators of the fuzzy logic model were used. The result of the fuzzy community operator generated the maximum membership membership membership. The fuzzy share operator extracts the minimum membership membership. The result of the operator of the fuzzy algebraic multiplication is reduced to zero numbers. The output map of the operator of the fuzzy algebra sum of the value of the pixels is close to the maximum. In order to modulate the very high sensitivity of the fuzzy algebraic operator and the very low accuracy of the fuzzy algebraic operator, a 0.9% gamma-gamma operator was used. Kappa coefficient for artificial neural network model was 0.83 and for fuzzy logic model 0.66. Conclusion The evaluation of the results obtained from the fuzzy logic model and the artificial neural network using kappa statistical coefficient shows that the artificial neural network with Kappa statistical coefficient is 0.91 compared to the fuzzy logic model with a kappa coefficient of 0.88 more than the prediction of the risk of landslide In the Seymareh Chenar Basin. Based on the zoning, the artificial neural network model was 10.12, 22.92, 31.44, 20.76, 15.16 percent of the area in the low, medium, high and very high risk classes has it. زمین‌لغزش به عنوان یکی از مخاطرات طبیعی در مناطق کوهستانی هر ساله منجر به خسارات زیادی می‌شود. حوضه آبریز سیمره چنار، با داشتن ویژگی های کوهستانی و شرایط طبیعی مختلف دارای استعداد بالقوه زمین‌لغزش است. هدف از این پژوهش، مقایسه مدل شبکه عصبی مصنوعی با مدل منطق فازی، جهت ارزیابی خطر زمین لغزش در حوضه سیمره چنار است. بدین جهت ابتدا پارامترهای مؤثر در وقوع زمین‌لغزش استخراج و سپس لایه‌های مربوطه تهیه‌ شده است. سپس نقشه پراکنش زمین‌لغزش‌های رخ‌داده شده حوضه تهیه و با تلفیق نقشه عوامل مؤثر بر لغزش با نقشه پراکنش زمین‌لغزش‌ها، تأثیر هر یک از عوامل شیب، جهت شیب، سنگ‌شناسی، بارش، فاصله از گسل، کاربری اراضی، خاک، فاصله از آبراهه در محیط نرم‌افزار ArcGIS محاسبه گردید. در این مطالعه به منظور مقایسه مدل‌ها، در پهنه‌بندی خطر زمین‌لغزش حوضه سیمره چنار، از مدل‌های شبکه عصبی مصنوعی و منطق فازی استفاده گردید. در مدل‌ شبکه عصبی مصنوعی الگوریتم پس انتشار خطا و تابع فعال‌سازی سیگموئید بکار گرفته شد. ساختار نهایی شبکه دارای 8 نرون در لایه ورودی، 14 نرون در لایه پنهان و 1 نرون در لایه خروجی گردید. پس از بهینه شدن ساختمان شبکه، کل اطلاعات منطقه در اختیار شبکه قرار گرفت و در نهایت با توجه به وزن خروجی، نقشه پهنه‌بندی زمین‌لغزش تهیه شد. در مدل منطق فازی از اپراتورهای عملگراجتماعفازی، عملگراشتراکفازی، عملگرضربجبری فازی، عملگرجمعجبری فازی، عملگرگاما فازی مدل منطق فازی استفاده شد. برای ارزیابی نتایج خروجی مدل‌های مورد استفاده در برآورد خطر لغزش منطقه از ضریب آماری کاپا استفاده شد. نتایج بدست آمده نشان می‌دهد که مدل شبکه عصبی مصنوعی با ضریب کاپای 91/0 مدل کارآمدتری نسبت به مدل منطق فازی در تهیه نقشه خطر لغزش‌های حوضه سیمره چنار است. از میان عوامل تاثیرگذار بر زمین لغزش در منطقه مورد مطالعه عامل شیب به عنوان مهمترین عامل و عوامل سنگ‌شناسی و خاک در مراتب بعدی قرار گرفتند. بر اساس پهنه‌بندی صورت گرفته با استفاده از مدل شبکه عصبی مصنوعی، به ترتیب 12/10، 92/22، 04/31، 76/20، 16/15 درصد از مساحت منطقه در کلاس‌های خطر خیلی‌کم، کم، متوسط، زیاد و خیلی‌زیاد قرار گرفته است.

https://www.geomorphologyjournal.ir/article_81029_7ad10b24b052f5f58ec1baf840a769d3.pdf