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Geosciences 2018, 8(5), 153; https://doi.org/10.3390/geosciences8050153

Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal)

1
Centre for Information and Seismovolcanic Surveilance of the Azores, University of the Azores, Rua Mãe de Deus, 9500-501 Ponta Delgada, Portugal
2
Research Institute for Volcanology and Risk Assessment, University of the Azores, Rua Mãe de Deus, 9500-501 Ponta Delgada, Portugal
*
Author to whom correspondence should be addressed.
Received: 22 March 2018 / Revised: 24 April 2018 / Accepted: 25 April 2018 / Published: 27 April 2018
(This article belongs to the Special Issue Natural Hazards and Risks Assessment)
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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. View Full-Text
Keywords: falls; slides; susceptibility analysis; success and prediction rate curves; Information Value; Azores falls; slides; susceptibility analysis; success and prediction rate curves; Information Value; Azores
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Silva, R.F.; Marques, R.; Gaspar, J.L. Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal). Geosciences 2018, 8, 153.

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