Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping

Abstract

Landslide susceptibility mapping (LSM) is a major area of interest within the field of disaster risk management that involves planning and decision-making activities. Therefore, preparation of dataset, construction of predictive model and analysis of results are considered to be important stages for effective and efficient disaster management in LSM. In recent years, a large number of studies has mainly focused on the effects of using machine learning (ML) algorithms as a predictive model in LSM. Decision tree-based ensemble learning algorithms known as decision forest is one of the popular ML techniques based on a combination of several decision tree algorithms to construct an optimal prediction model. In this study, prediction performances of recently proposed decision tree-based ensemble-based algorithms namely canonical correlation forest (CCF) and rotation forest (RotFor) are tested on LSM. In order to compare their performances, popular ensemble learning algorithms including random forest (RF), AdaBoost and bagging algorithms are also considered. For this purpose, first, twelve conditioning factors are determined in the study area, Karabuk province of Turkey. Second, individual importance of the factors on LSM process is evaluated using Fischer score analysis and selected factors are used as an input dataset for the construction of landslide susceptibility prediction models of CCF, RotFor, RF, AdaBoost and bagging algorithms. For the assessment of the performances, overall accuracy (OA), success rate curves and the area under the curve (AUC) analysis are utilized. Furthermore, chi-squared-based McNemar’s test and well-known accuracy measures known as receiver operating characteristic (ROC) curves are employed to evaluate the pairwise comparison of the ensemble learning methods. Results show that CCF method outperforms the RotFor method by about 4%, and there is no statistically significant difference between CFF and other methods.