Ấn phẩm:
Assessing the relationship between landslide susceptibility and land cover change using machine learning
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Tóm tắt
Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying the landslide occurrence probability within the region is essential in supporting decision-makers or developers in establishing effective strategies for reducing the damage. This study is aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), and Bagging (BA) for assessing the connection of land cover change to landslide susceptibility in Da Lat City, Vietnam. 202 landslide locations and 13 potential drivers became input data for the model. Various statistical indices, namely the root mean square error (RMSE), the area under the curve (AUC), and mean absolute error (MAE), were used to evaluate the proposed models. Our findings indicate that the Xgboost model was better than other models, as shown by the AUC value of 0.94, followed by LightGBM (AUC=0.91), KNN (AUC=0.87), and Bagging (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² to 30 km² in very high landslide susceptibility areas. Our approach can be applied to test the other regions in Vietnam. Our findings might represent a necessary tool for land use planning strategies to reduce damage from natural disasters and landslides.
Mô tả
Vietnam Journal of Earth Sciences, Vol. 46, No. 3
Tác giả
Nguyen, Huu Duy
Vu, Cong Tung
Bretcan, Petre
Petrisor, Alexandru Ionut
Người hướng dẫn
Nơi xuất bản
Nhà xuất bản
Viện Địa chất, Viện Hàn lâm Khoa học và Công nghệ Việt Nam
Năm xuất bản
2024-06
ISSN tạp chí
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Từ khóa chủ đề
Machine learning , Landslide susceptibility , Da Lat city
URI
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Thông tin bản quyền
Tệp tin
Huu Duy Nguyen, Tung Cong Vu, Petre Bretcan, Alexandru-Ionut Petrisor.pdf
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