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A probabilistic procedure to define multidimensional rainfall thresholds for territorial landslide warning models
https://orcid.org/0000-0003-2264-9698
Abstract A procedure designed to develop probabilistic thresholds for rainfall-induced landslides adopting novel rainfall variables is proposed and tested in an area of Campania region, southern Italy. The dataset used comprises 180 rainfall-induced landslides in the period 2010–2020, derived from the FraneItalia catalogue (https://franeitalia.wordpress.com/), and rainfall records retrieved from a network of 22 regional rain gauges active in the area. The procedure starts by reconstructing rainfall events responsible and not responsible for landslides in the period of analysis. Subsequently, a large number of rainfall variables are derived automatically, adopting a time series feature extraction tool, and their significance in identifying landslide-triggering events is evaluated. Then, adopting a Bayesian approach, the most significant rainfall variables are used to define a set of probabilistic thresholds in one, two, and three dimensions. Finally, the most effective thresholds are identified by means of standard performance indicators. The probabilistic thresholds developed using novel rainfall variables outperform those employing conventional variables. Specifically, looking at the minimum distance from the perfect classification point (δ), thresholds employing novel variables yield a minimum δ of 0.230, while those adopting conventional variables lead to a minimum δ of 0.292. The results achieved herein demonstrate that the use of novel rainfall variables within territorial landslide warning models can represent a promising option for improving the performance of these models.
A probabilistic procedure to define multidimensional rainfall thresholds for territorial landslide warning models
https://orcid.org/0000-0003-2264-9698
Abstract A procedure designed to develop probabilistic thresholds for rainfall-induced landslides adopting novel rainfall variables is proposed and tested in an area of Campania region, southern Italy. The dataset used comprises 180 rainfall-induced landslides in the period 2010–2020, derived from the FraneItalia catalogue (https://franeitalia.wordpress.com/), and rainfall records retrieved from a network of 22 regional rain gauges active in the area. The procedure starts by reconstructing rainfall events responsible and not responsible for landslides in the period of analysis. Subsequently, a large number of rainfall variables are derived automatically, adopting a time series feature extraction tool, and their significance in identifying landslide-triggering events is evaluated. Then, adopting a Bayesian approach, the most significant rainfall variables are used to define a set of probabilistic thresholds in one, two, and three dimensions. Finally, the most effective thresholds are identified by means of standard performance indicators. The probabilistic thresholds developed using novel rainfall variables outperform those employing conventional variables. Specifically, looking at the minimum distance from the perfect classification point (δ), thresholds employing novel variables yield a minimum δ of 0.230, while those adopting conventional variables lead to a minimum δ of 0.292. The results achieved herein demonstrate that the use of novel rainfall variables within territorial landslide warning models can represent a promising option for improving the performance of these models.
A probabilistic procedure to define multidimensional rainfall thresholds for territorial landslide warning models
https://orcid.org/0000-0003-2264-9698
Abstract A procedure designed to develop probabilistic thresholds for rainfall-induced landslides adopting novel rainfall variables is proposed and tested in an area of Campania region, southern Italy. The dataset used comprises 180 rainfall-induced landslides in the period 2010–2020, derived from the FraneItalia catalogue (https://franeitalia.wordpress.com/), and rainfall records retrieved from a network of 22 regional rain gauges active in the area. The procedure starts by reconstructing rainfall events responsible and not responsible for landslides in the period of analysis. Subsequently, a large number of rainfall variables are derived automatically, adopting a time series feature extraction tool, and their significance in identifying landslide-triggering events is evaluated. Then, adopting a Bayesian approach, the most significant rainfall variables are used to define a set of probabilistic thresholds in one, two, and three dimensions. Finally, the most effective thresholds are identified by means of standard performance indicators. The probabilistic thresholds developed using novel rainfall variables outperform those employing conventional variables. Specifically, looking at the minimum distance from the perfect classification point (δ), thresholds employing novel variables yield a minimum δ of 0.230, while those adopting conventional variables lead to a minimum δ of 0.292. The results achieved herein demonstrate that the use of novel rainfall variables within territorial landslide warning models can represent a promising option for improving the performance of these models.
A probabilistic procedure to define multidimensional rainfall thresholds for territorial landslide warning models
Landslides
Zhang, Sen (author) / Pecoraro, Gaetano (author) / Jiang, Qigang (author) / Calvello, Michele (author)
2025-02-11
Article (Journal)
Electronic Resource
English
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