Landslide Susceptibility
Analysis using Gradient Boosting Models: A Case Study in Penang Island, Malaysia
Gao Han, Fam Pei Shan, Tay Lea Tien and Low Heng Chin
Disaster Advances; Vol. 14(8); 22-37;
doi: https://doi.org/10.25303/148da2221; (2021)
Abstract
Tree-based gradient boosting (TGB) models gain popularity in various areas due to
their powerful prediction ability and fast processing speed. This study aims to
compare the landslide spatial prediction performance of TGB models and non-tree-based
machine learning (NML) models in Penang Island, Malaysia. Two specific instances
of TGB models, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine
(LightGBM) and two specific instances of NML models, artificial neural network (ANN)
and support vector machine (SVM), are applied to make predictions of landslide susceptibility.
Feature selection and oversampling techniques are considered to improve the prediction
performance as well. The results are analyzed and discussed mainly based on receiver
operating characteristic (ROC) curves as well as the area under the curves (AUC).
The results show that TGB models give better prediction performance compared to
NML models, no matter what the sample size is. The TGB models’ performances are
improved when training with the dataset considering either feature selection or
oversampling techniques. The highest AUC value of 0.9525 is obtained from the combination
of XGBoost and SMOTE. The landslide susceptibility maps (LSMs) produced by XGBoost
and LightGBM can provide valuable information in landslide management and mitigation
in Penang Island, Malaysia.
