Landslide susceptibility maps resulted form the modelling of landslide susceptibility are key tools in the management of crisis in landslide prone areas. In the modelling of landslide susceptibility variety of heuristic and statistically based approaches being used.
In the worldwide landslide prone areas we need more accurate landslide susceptibility maps in order to be logistically better prepared in the case of crisis management. Recently dynamic modelling of time-require landslide susceptibility was introduced and implemented in Collazzone study area in Italy. The model was constructed using multiple logistic regression. Nowadays machine learning-based approaches can better capture the importance of conditioning attributes relevant in the modelling of landslide susceptibility leading to more accurate landslide susceptibility model. Modelling of time-variant landslide susceptibility using machine-learning-based approach could be interesting to compare the performance of the model with statistically-based landslide susceptibility model.
- Explore the importance of landslide path dependency variables in comparison with conditioning attributes using machine learning approaches
- Modelling of time-variant landslide susceptibility using machine-learning approach
- Comparing the performance of landslide susceptibility model constructed by logistic regression with the performance of model constructed by machine learning-based approach
- Samia, J., Temme, A., Bregt, A., Wallinga, J., Guzzetti, F., Ardizzone, F., and Rossi, M.: Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory, Landslides, 14, 547-558, 2017a.
- Samia, J., Temme, A., Bregt, A., Wallinga, J., Guzzetti, F., Ardizzone, F., and Rossi, M.: Characterization and quantification of path dependency in landslide susceptibility, Geomorphology, 292, 16-24, 2017b.
- Samia, J., Temme, A., Bregt, A. K., Wallinga, J., Stuiver, J., Guzzetti, F., Ardizzone, F., and Rossi, M.: Implementing landslide path dependency in landslide susceptibility modelling, Landslides, 1-16, 2018.
- Interest in spatial-temporal modelling in the context of natural disasters
Theme(s): Modelling & visualisation