Abstract
The main objective of this study is to better understand and quantify the consequences for landslide susceptibility assessment caused by (i) the discrimination (or not) of landslide typology and (ii) the use of different predisposing factor combinations. The study area for this research was Lajedo parish (Flores Island, Azores—Portugal). For the landslide susceptibility modeling, 12 predisposing factors and a historical landslide inventory with a total of 474 individual landslide rupture areas were used as inputs, and the Information Value method was then applied. It was concluded that susceptibility models developed specifically for each landslide typology achieve better results when compared to the model developed for the total inventory, which suffers from a bias caused by the strong spatial abundance of one landslide typology. A total of 4095 susceptibility models were tested for each typology, and the best models were selected according to their goodness of fit. The best model for both falls and slides has seven predisposing factors, some of which do not correspond to the factors that have the best individual discriminatory capabilities. The number of expected and observed unique terrain conditions for each model allowed us to conclude that with the successive addition of predisposing factors, there is an inability of the territory to generate new observed unique terrain conditions. This consequence was directly related to the inability to increase the goodness of fit of the computed models. For each landslide typology, the predictive capacity of the best susceptibility model was assessed by computing the Prediction Rate Curves and the Area Under the Curve.
Highlights
Landslides are responsible for significant economic and social impacts in many areas of the world.They cause the death of people and animals, and destruction or damage of residential and industrial infrastructures and agricultural areas [1]
FALL-MOD, as well as for SLD-TOT and SLD-MOD, are very similar; and (ii) there are significant differences in the computed Ii values for different landslide typologies, which reinforces the importance of considering the landslide typologies independently for modeling landslide susceptibility, as shown by other authors
The results obtained in this paper demonstrate that the influence and the weight of the predisposing factors for landslide susceptibility analysis are very different depending on the landslide typology, and support (a) the importance of discriminating landslides (b) by typology
Summary
Landslides are responsible for significant economic and social impacts in many areas of the world. They cause the death of people and animals, and destruction or damage of residential and industrial infrastructures and agricultural areas [1]. The identification of areas that are susceptible to landslides is important for land-use and emergency planning [2]. Risk assessment of the multi-hazards in an area can focus on selection of appropriate sites for urban development and can prevent the migration of people and urban sprawl to dangerous places [3,4]. Regardless of some uncertainties, the production and validation of susceptibility, vulnerability, and risk maps make an important contribution to public safety [2].
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