کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8908228 | 1635347 | 2018 | 46 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods
ترجمه فارسی عنوان
مدل سازی حساسیت زمینلغزش در محیطهای داده کم با استفاده از داده کاوی بهینه و روشهای آماری
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
فرآیندهای سطح زمین
چکیده انگلیسی
This study evaluated the generalizability of five models to select a suitable approach for landslide susceptibility modeling in data-scarce environments. In total, 418 landslide inventories and 18 landslide conditioning factors were analyzed. Multicollinearity and factor optimization were investigated before data modeling, and two experiments were then conducted. In each experiment, five susceptibility maps were produced based on support vector machine (SVM), random forest (RF), weight-of-evidence (WoE), ridge regression (Rid_R), and robust regression (RR) models. The highest accuracy (AUCÂ =Â 0.85) was achieved with the SVM model when either the full or limited landslide inventories were used. Furthermore, the RF and WoE models were severely affected when less landslide samples were used for training. The other models were affected slightly when the training samples were limited.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Geomorphology - Volume 303, 15 February 2018, Pages 284-298
Journal: Geomorphology - Volume 303, 15 February 2018, Pages 284-298
نویسندگان
Jung-Hyun Lee, Maher Ibrahim Sameen, Biswajeet Pradhan, Hyuck-Jin Park,