Landslide inventory, sampling and effect of sampling strategies on landslide susceptibility/hazard modelling at a glance

Abstract

Landslides have a significant portion of responsibility on the damages and losses caused by natural hazards such as earthquakes, floods, storms, and tsunamis all over the world. Thus, landslides and their consequences are of great importance among the scientists and authorities who want to minimize these effects for a long time. This procedure simply begins with the preparation of landslide database and inventory maps, which constitutes a fundamental basis for the further steps including landslide susceptibility, hazard, and risk assessments. In this aspect, this procedure can be considered as one of the most important stages for any landslide work to minimize the undesired consequences of landslides. This stage can be realized using some statistical techniques such as simple random, systematic, stratified and cluster sampling strategies in the literature. In this chapter, firstly, basic landslide definitions and concepts were discussed. Then, landslide inventory, susceptibility and hazard concepts were pointed out and linked to the sampling strategies with the recent literature. Although, every considered method has pros and cons, it could be concluded that the sampling carried out in the rupture zones of landslides as polygon features or seed cell approach representing the pre-failure conditions seem to be more realistic to obtain more accurate maps. The other important issue pointed out in this chapter is on the selection of data mining technique(s). Since landslides are complex processes and can be affected by many factors, this stage is very important to reflect the landslide conditions with huge amount of data. In many cases, the researchers generally encounter to struggle with huge amount of data related to the landslide initiation and/or mechanisms. Thus, the selection of data mining techniques deserve the necessary precaution and is elaborately discussed overall the chapter. © Springer Nature Switzerland AG 2019.