Landslide susceptibility assessment using SVM machine learning algorithm: Fid-Bau Portal

Landslide susceptibility assessment using SVM machine learning algorithm

Marjanović, Miloš / Kovačević, Miloš / Bajat, Branislav / Voženílek, Vít

Abstract This paper introduces the current machine learning approach to solving spatial modeling problems in the domain of landslide susceptibility assessment. The latter is introduced as a classification problem, having multiple (geological, morphological, environmental etc.) attributes and one referent landslide inventory map from which to devise the classification rules. Three different machine learning algorithms were compared: Support Vector Machines, Decision Trees and Logistic Regression. A specific area of the Fruška Gora Mountain (Serbia) was selected to perform the entire modeling procedure, from attribute and referent data preparation/processing, through the classifiers' implementation to the evaluation, carried out in terms of the model's performance and agreement with the referent data. The experiments showed that Support Vector Machines outperformed the other proposed methods, and hence this algorithm was selected as the model of choice to be compared with a common knowledge-driven method – the Analytical Hierarchy Process – to create a landslide susceptibility map of the relevant area. The SVM classifier outperformed the AHP approach in all evaluation metrics (κ index, area under ROC curve and false positive rate in stable ground class).

Highlights ► After conducting experiments based on the sampling over the entire area, the SVM turned to be the model of choice. ► In the mapping task on a neighboring terrain, SVM achieved better performance than AHP model in all evaluation measures. ► The results of the 2nd mapping task were weaker in comparison to the 1st one (different distribution of input parameters). ► SVM do not need any feature selection technique as opposed to some other methods such as Decision Trees.