Search

Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. However, few studies have been published on the training data selection effects on the LSM. Thus, this study assesses the training landslides random selection effects on support vector machine (SVM) accuracy, logistic regression (LR) and artificial neural networks (ANN) models for LSM in a catchment at the Dodangeh watershed, Mazandaran province, Iran. A 160 landslide locations inventory was collected by Geological Survey of Iran for this investigation. Different methods were implemented to define the landslide locations, such as inventory reports, satellite images and field survey. Moreover, 14 landslide conditioning factors were considered in the analysis of landslide susceptibility. These factors include curvature, plan curvature, profile curvature, altitude, slope angle, slope aspect, distance to faults, distance to stream, topographic wetness index, stream power index, terrain roughness index, sediment transport index, lithology and land use. The results show that the random landslide training data selection affected the parameter estimations of the SVM, LR and ANN algorithms. The results also show that the training samples selection had an effect on the accuracy of the susceptibility model because landslide conditioning factors vary according to the geographic locations in the study area. The LR model was found to be less sensitive than the SVM and ANN models to the training samples selection. Validation results showed that SVM and LR models outperformed the ANN model for all scenarios. The average overall accuracy of LR, SVM and ANN models are 81.42%, 79.82% and 70.2%, respectively. [ABSTRACT FROM AUTHOR]

A decision tree approach was applied and validated for analysis of landslide susceptibility using a geographic information system (GIS). The study area was the Pyeongchang area in Gangwon Province, Korea, where many landslides occurred in 2006 and where the 2018 Winter Olympics are to be held. Spatial data, such as landslides, topography, and geology, were detected, collected, and compiled in a database using remote sensing and GIS. The 3994 recorded landslide locations were randomly split 50/50 for training and validation of the models. A decision tree model, which is a type of data-mining classification model, was applied and decision trees were constructed using the chi-squared (χ2) automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. Also, as a reference, a frequency-ratio model was applied using the same database. The relationships between the detected landslide locations and their factors were identified and quantified by frequency-ratio and decision tree models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indices and maps. Then, the resulting landslide-susceptibility maps were validated using area-under-the-curve (AUC) analysis with the landslide area data that had not been used for training the model. The decision tree models using the CHAID and QUEST algorithms had accuracies of 81.56% and 80.91%, respectively, which were somewhat better than the results for the frequency-ratio model (80.15%). These results indicate that decision tree models using the CHAID and QUEST algorithms can be useful for landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]