Tên bài báo:

Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam
Tác giả:
Phạm Thái Bình
Tham gia cùng:
Lý Hải Bằng
Tạp chí:
Geocarto International
Năm xuất bản:
Từ trang 1 đến trang 25
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

Landslide is a natural hazard which causes huge loss of properties and human life in many places of the world. Mapping of landslide susceptibility is an important task for preventing and combating the landslides problems. Main objective of this study is to use different artificial intelligence methods namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), and reduced error-pruning tree (REPT) in the development of models for landslide susceptibility mapping of Muong Lay district of Vietnam. In total data of 217 landslide locations of the study area was used for the development and evaluation of the models. Nine landslide-conditioning factors were used for generating the datasets for training and validating the models. Results show that the SVM outperformed all other methods namely ANN, LR and REPT. Thus, it can be suggested that the SVM method is more useful in developing accurate and robust landslide prediction model.

Từ khóa:

Landslide artificial intelligence artificial neural network logistic regression support vector machines
Thông tin tác giả
Phạm Thái Bình

Phạm Thái Bình

Thạc sĩ kỹ thuật

Lý lịch khoa học