ANALYSIS OF DEBRIS FLOW BEHAVIOR USING AIRBORNE LIDAR AND IMAGE DATA

To reduce the consequences of landslides due to rainfall, such as loss of life, economic losses, and disruption to daily living, this study describes the process of building a machine learning model which can help to estimate the volume of landslide material that can occur in a particular region, taking into account antecedent rainfall, soil characteristics, type of vegetation, etc. The findings can be useful for land use management, infrastructure design, and rainfall disaster management.

The prediction of the size of rainfall-induced debris in South Korea was analyzed. The model suitability was carried out and Random forest was the most suitable for the Size of debris prediction. The most contributing factor in the model was slope length and the most vulnerable region to higher frequency and severe debris was Gangwon province. The findings may be used for rainfall induced-debris prevention policies and post-disaster rehabilitation planning.