Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal)
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 p...
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doaj-de7f3c0ed16b4216b191de088ca7cb6c2020-11-24T23:16:18ZengMDPI AGGeosciences2076-32632018-04-018515310.3390/geosciences8050153geosciences8050153Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal)Rui Fagundes Silva0Rui Marques1João Luís Gaspar2Centre for Information and Seismovolcanic Surveilance of the Azores, University of the Azores, Rua Mãe de Deus, 9500-501 Ponta Delgada, PortugalCentre for Information and Seismovolcanic Surveilance of the Azores, University of the Azores, Rua Mãe de Deus, 9500-501 Ponta Delgada, PortugalResearch Institute for Volcanology and Risk Assessment, University of the Azores, Rua Mãe de Deus, 9500-501 Ponta Delgada, PortugalThe 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.http://www.mdpi.com/2076-3263/8/5/153fallsslidessusceptibility analysissuccess and prediction rate curvesInformation ValueAzores |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rui Fagundes Silva Rui Marques João Luís Gaspar |
spellingShingle |
Rui Fagundes Silva Rui Marques João Luís Gaspar Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal) Geosciences falls slides susceptibility analysis success and prediction rate curves Information Value Azores |
author_facet |
Rui Fagundes Silva Rui Marques João Luís Gaspar |
author_sort |
Rui Fagundes Silva |
title |
Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal) |
title_short |
Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal) |
title_full |
Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal) |
title_fullStr |
Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal) |
title_full_unstemmed |
Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal) |
title_sort |
implications of landslide typology and predisposing factor combinations for probabilistic landslide susceptibility models: a case study in lajedo parish (flores island, azores—portugal) |
publisher |
MDPI AG |
series |
Geosciences |
issn |
2076-3263 |
publishDate |
2018-04-01 |
description |
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. |
topic |
falls slides susceptibility analysis success and prediction rate curves Information Value Azores |
url |
http://www.mdpi.com/2076-3263/8/5/153 |
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