The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery

Logistic model tree (LMT), a new method integrating standard decision tree (DT) induction and linear logistic regression algorithm in a single tree, have been recently proposed as an alternative to DT-based learning algorithms. In this study, the LMT was applied in the context of pixel- and object-based classifications using high-resolution WorldView-2 imagery, and its performance was compared with C4.5, random forest and Adaboost. Results of the study showed that the LMT generally produced more accurate classification results than the other methods for both pixel- and object-based classifications. The improvement in classification accuracy reached to 3% in pixel-based and 5% in object-based classifications. It was also estimated that the LMT algorithm produced the most accurate results considering the allocation and overall disagreement errors. Based on the Wilcoxon’s Signed-Ranks tests, the performance differences between the LMT and the other methods were statistically significant for both pixel- and object-based image classifications

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

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

A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping

In recent years, ensemble learning methods have become popular in landslide susceptibility mapping (LSM) with varying degrees of success. Within classifier ensemble concept, decision tree based ensemble learners such as random forest (RF) (i.e. decision forest) and rotation forest (RotFor) have gained a great interest due to their robustness against conventional statistical methods. This study proposes canonical correlation forest (CCF), a new member of ensemble learning family, in the prediction of landslide susceptibility for Yenice district of Karabuk in Turkey. To test the robustness and suitability of the CCF method, its prediction performance was compared to two well-known machine learning ensemble algorithms, RF and RotFor, and a commonly used statistical method, the logistic regression (LR). Furthermore, the effects of variations in ratio of training/testing datasets were assessed on the performances of RF, CCF, RotFor and LR models using the root-mean square error (RMSE). The quality of resulting landslide susceptibility maps was evaluated using overall accuracy (OA), Kappa coefficient (KC), success rate curves and receiver operating characteristic (ROC) curves. Wilcoxon’s signed rank test was also applied to measure the statistical differences of the accuracies of susceptibility maps. The estimated area under curve (AUC) values for RF, CCF, RotFor and LR models were 0.982, 0.970, 0.966 and 0.826, respectively. It was clear that ensemble learning algorithms outperformed the LR method. The results also showed that selection of sampling ratio had significant effect on model performance of RF, CCF, RotFor and LR models, and the lowest RMSE values were estimated with the use of 70:30 ratio for training and test datasets