21st EANN 2020, 5 -7 June 2020, Greece

Classification of Coseismic Landslides using Fuzzy and Machine Learning Techniques

Anastasios Panagiotis Psathas, Antonios Papaleonidas, George Papathanassiou, Sotiris Valkaniotis, Lazaros Iliadis


  Seismically generated landslides represent one of the most damaging hazards associated with earthquakes in countries with high seismicity. The delineation of prone to coseismic landsliding areas is crucial in order to predict the occurrence of earthquake-induced landslides and consequently reduce the relevant risk. The goal of this study is to investigate the correlation of the pattern of coseismic landslides with geological and topographical variables i.e. lithology, slope angle and slope aspect with the volume of landslides based on fuzzy logic and machine learning techniques. For this task, a real dataset of 421 and 767 instances for years 2003 and 2015 respectively from the island of Lefkada was used. A new approach based on Fuzzy C-Means Algorithm and Ensemble Subspace k- Nearest-Neighbors (Ensemble Subspace k-NN) is proposed. Landslides were classified according to their severity with a success rate of 99.5% and 98.7% for 2003 and 2015 respectively. The performance of the proposed approach was evaluated using “One Versus All” Strategy, calculating Accuracy, Sensitivity, Specificity, Precision and F-1 Score for each cluster.  

*** Title, author list and abstract as seen in the Camera-Ready version of the paper that was provided to Conference Committee. Small changes that may have occurred during processing by Springer may not appear in this window.