Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam

Abstract

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.

Keywords:

• Landslide
• artificial intelligence
• artificial neural network
• logistic regression
• support vector machines

Acknowledgment

We thank Dr. Jonathan Josephs-Spaulding for his useful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

TPT thanks the support from senior research assistant program (VAST) and national project “Pliocene-present tectonics in Vietnam islands and continental shelf for assessing geological hazards”, KC.09.22/16-20.