Landslide susceptibility mapping using state-of-the-art machine learning ensembles

• Published in: Geocarto international 2022-09, Vol.37 (18), p.5175-5200
• Main Authors: Pham, Binh Thai, Vu, Vinh Duy, Costache, Romulus, Phong, Tran Van, Ngo, Trinh Quoc, Tran, Trung-Hieu, Nguyen, Huu Duy, Amiri, Mahdis, Tan, Mai Thanh, Trinh, Phan Trong, Le, Hiep Van, Prakash, Indra
• Format: Article
• Language: English
• Subjects: Landslides; machine learning; Quang Nam province; susceptibility mapping; Vietnam
• ISSN: 1010-6049; 1752-0762
• DOI: 10.1080/10106049.2021.1914746

This study propose a new approach through which the landslide susceptibility in Quang Nam (Vietnam) will be estimated using the best model among the following algorithms: Decision Table (DT), Naïve Bayes (NB), Decision Table - Naïve Bayes (DTNB), Bagging Ensemble, Cascade Generalization Ensemble, Dagging Ensemble, Decorate Ensemble, MultiBoost Ensemble, MultiScheme Ensemble, Real Ada Boost Ensemble, Rotation Forest Ensemble, Random Sub Space Ensemble. In this regard, a map with 1130 landslide, was created and further partitioned into training (70%) and testing (30%) locations. The correlation-based features selections (CFS) method was used to select a number of 15 landslide influencing factors. Landslide locations, included in the training sample, and the landslide predictors were used as input data in order to run the above mentioned models. Kappa index, Accuracy (%) and ROC curve were employed to estimate the model's performance and to test the outcomes provided by the models. Among the eleven machine learning algorithms, Random Sub Space Decision Table Naïve Bayes (RSSDTNB) was the most performant model with an AUC = 0.839, Accuracy = 76.55% and Kappa Index = 0.531. Therefore, this algorithm was involved in the estimation of landslide susceptibility. The Success Rate (AUC = 0.815) and Prediction Rate (AUC = 0.826) revealed the achievement of high-quality results.