Aquilegia, Vol. 13 No. 1, January-February 1989: Newsletter of the Colorado Native Plant Society

https://epublications.regis.edu/aquilegia/1044/thumbnail.jp

A Study on Development of Landslide Susceptibility Map in Malaysia Landslide Prone Areas by Using Geographic Information System (GIS) and Machine Learning

Landslide is a natural disaster that is common and frequently occurring in Malaysia. Thus, to reduce the impact of the landslide’s tragedy, a landslide susceptibility map is needed. The ultimate goal of this paper is to use Geographic Information System (GIS) and machine learning to develop a landslide susceptibility map. In two different landslide-prone areas in Malaysia, the performance of the two different machine learning models, Random Forest and Extreme Gradient Boosting (XGBoost) are evaluated and cross-validated. The Cameron Highland and Penang Island, Malaysia which are the subjects of this study, have a total of 233 and 443 landslides locations, respectively. These landslide locations were randomly divided into 70% for training and 30% for testing. The Digital Elevation Model (DEM), slope angle, slope length, Normalized Vegetation Index (NDVI), plan curvature, profile curvature, distance from the stream, distance from roads, Topographic Wetness Index (TWI) and Stream Power Index (SPI) are among the ten landslide conditioning factors, for which the spatial databases were developed by using GIS software. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) had been applied to evaluate the machine learnings prediction accuracy. The result indicated that both XGBoost and Random Forest had a great performance across both study areas. For Penang Island, the AUC of XGBoost is 95.02% and the AUC of Random Forest is 94.99%. Meanwhile, for Cameron Highland, the AUC of XGBoost is 91.99% and the AUC of Random Forest is 92.32%. The final prediction map from this study might be useful for better planning in mitigating the occurrence of landslide

A Study on Performance Comparisons between KNN, Random Forest and XGBoost in Prediction of Landslide Susceptibility in Kota Kinabalu, Malaysia

One of the most natural catastrophes in Malaysia, landslides, has resulted in several fatalities, infrastructure damage and economic losses. Over time, researchers have used various methods to forecast the vulnerability to landslides. Unfortunately, the most accurate algorithm which can be used to develop a landslide susceptibility model is still lacking. Therefore, the current study aims to evaluate how well Kota Kinabalu, Sabah's landslide susceptibility, can be predicted using three different machine learning techniques: K-Nearest Neighbor (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost). The research areas had 242 landslide locations, and the inventory data was arbitrarily separated into training and testing datasets in a 7/3 ratio. As prediction parameters, ten spatial databases of landslides conditioning factors were employed. The area under the curve (AUC) was utilized as the models’ performance metric. With an AUC score of 87.52 %, the final analysis showed that KNN had the highest prediction accuracy, followed by Random Forest (84.34 %) and XGBoost (78.07%). According to the AUC findings, KNN, Random Forest, and XGBoost performed consistently well in forecasting landslide susceptibility. The final forecast map can be a helpful tool for urban planning and development and for aiding the authorities in creating a strategic mitigation plan