03 خرداد 1403
مهيار يوسفي

مهیار یوسفی

مرتبه علمی: دانشیار
نشانی:
تحصیلات: دکترای تخصصی / مهندسی معدن-اکتشافات معدن
تلفن:
دانشکده: دانشکده فنی مهندسی

مشخصات پژوهش

عنوان
Landslide risk potential mapping by using continuously-weighted spatial criteria and convolution artificial neural network
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Convolution neural network GIS Logistics function prediction-area plot diagram Zoning
سال
2023
مجله Scientia Iranica
پژوهشگران واحد قیاسی ، مهیار یوسفی

چکیده

Landslides are one of the most dangerous natural phenomena. The occurrence of this phenomenon at low speeds and high rates causes financial and human losses without warning signs. Therefore, it is essential to study the geological and anthropogenic factors affecting the occurrence of this phenomenon and determine the potential landslide zones. This study aims to use a supervised convolutional artificial neural network to model landslide potential. For this, evidence maps of seven effective factors in landslide occurrence, including slope, slope direction, geology, precipitation, distance from the fault, height, and density of waterway, were prepared. Then the values in the maps were assigned by continuous fuzzy weights through a logistic function, without data classification to feed the convolution artificial neural network algorithm. For training the network and testing the results, 70% and 30% of training sites, in Oshvand basin, Hamedan province, Iran were used to generate landslide potential model. A prediction-area plot was used to evaluate and quantify the effectiveness of the models produced. The results showed that 70% of the landslides occurred in 30% of the area.