Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics

- Application of a viewshed to avoid a statistical bias due to a limited mapping area
- Detailed analysis of the relation of landslides to contributing parameters
- New weighting method of parameters, named omit error, leads to the best results.
- Useful susceptibility maps to plan or adapt landuse and infrastructure

Vietnam is regarded as a country strongly impacted by climate change. Population and economic growth result in additional pressures on the ecosystems in the region. In particular, changes in landuse and precipitation extremes lead to a higher landslide susceptibility in the study area (approx. 12,400 km2), located in central Vietnam and impacted by a tropical monsoon climate. Hence, this natural hazard is a serious problem in the study area. A probability assessment of landslides is therefore undertaken through the use of bivariate statistics. However, the landslide inventory based only on field campaigns does not cover the whole area. To avoid a systematic bias due to the limited mapping area, the investigated regions are depicted as the viewshed in the calculations. On this basis, the distribution of the landslides is evaluated in relation to the maps of 13 parameters, showing the strongest correlation to distance to roads and precipitation increase. An additional weighting of the input parameters leads to better results, since some parameters contribute more to landslides than others. The method developed in this work is based on the validation of different parameter sets used within the statistical index method. It is called “omit error” because always omitting another parameter leads to the weightings, which describe how strong every single parameter improves or reduces the objective function. Furthermore, this approach is used to find a better input parameter set by excluding some parameters. After this optimization, nine input parameters are left, and they are weighted by the omit error method, providing the best susceptibility map with a success rate of 92.9% and a prediction rate of 92.3%. This is an improvement of 4.4% and 4.2%, respectively, compared to the basic statistical index method with the 13 input parameters.