Tên bài báo:

Bagging based Support Vector Machines for spatial prediction of landslides
Tác giả:
Phạm Thái Bình
Tham gia cùng:
Tạp chí:
Environmental Earth Sciences
Năm xuất bản:
Từ trang 1 đến trang 17
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging Ensemble and Support Vector Machine (SVM) classifer, was proposed for the spatial prediction of landslides at the district of Mu Cang Chai, Viet Nam. In the present study, 248 past landslides and ffteen geo-environmental factors (curvature, elevation, distance to rivers, slope, aspect, river density, plan curvature, distance to faults, profle curvature, fault density, lithology, distance to roads, rainfall, land use, and road density) were considered for the model construction. Diferent evaluation criteria were applied to validate the proposed hybrid model such as statistical index-based methods and area under the receiver operating characteristic curve (AUC). The single SVM and the Naïve Bayes Trees (NBT) models were selected for comparison. Based on the AUC values, the proposed hybrid model BSVM (0.812) outperformed the SVM (0.804) and NBT (0.8) models. Thus, the BSVM is a promising and better method for landslide predictio

Từ khóa:

Landslides Machine learning Ensemble techniques Bagging Support Vector Machines
Thông tin tác giả
Phạm Thái Bình

Phạm Thái Bình

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