Assessing the relationship between landslide susceptibility and land cover change using machine learning DOI Open Access

Duy Nguyen Huu,

Tung Vu Cong,

Petre Brețcan

et al.

VIETNAM JOURNAL OF EARTH SCIENCES, Journal Year: 2024, Volume and Issue: unknown

Published: May 2, 2024

Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying landslide occurrence probability within is essential supporting decision-makers or developers establishing effective strategies for reducing damage. This study aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), Bagging (BA) assessing connection land cover change susceptibility Da Lat City, Vietnam. 202 locations 13 potential drivers became input data model. Various statistical indices, root mean square error (RMSE), area under curve (AUC), absolute (MAE), were used evaluate proposed models. Our findings indicate that model was better than other models, as shown by AUC value 0.94, followed LightGBM (AUC=0.91), KNN (AUC=0.87), (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² 30 very high areas. approach can be applied test regions might represent necessary tool use planning reduce landslides.

Language: Английский

Assessing the relationship between landslide susceptibility and land cover change using machine learning DOI Open Access

Duy Nguyen Huu,

Tung Vu Cong,

Petre Brețcan

et al.

VIETNAM JOURNAL OF EARTH SCIENCES, Journal Year: 2024, Volume and Issue: unknown

Published: May 2, 2024

Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying landslide occurrence probability within is essential supporting decision-makers or developers establishing effective strategies for reducing damage. This study aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), Bagging (BA) assessing connection land cover change susceptibility Da Lat City, Vietnam. 202 locations 13 potential drivers became input data model. Various statistical indices, root mean square error (RMSE), area under curve (AUC), absolute (MAE), were used evaluate proposed models. Our findings indicate that model was better than other models, as shown by AUC value 0.94, followed LightGBM (AUC=0.91), KNN (AUC=0.87), (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² 30 very high areas. approach can be applied test regions might represent necessary tool use planning reduce landslides.

Language: Английский

Citations

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