TY - UNPB
T1 - Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy
AU - Moreno, Mateo
AU - Lombardo, Luigi
AU - Steger, Stefan
AU - de Vugt, Lotte
AU - Zieher, Thomas
AU - Crespi, Alice
AU - Marra, Francesco
AU - van Westen, Cees
AU - Opitz, Thomas
PY - 2024/12/10
Y1 - 2024/12/10
N2 - Shallow landslides are geomorphic hazards in mountainous terrains across the globe. Their occurrence can be attributed to the interplay of static and dynamic landslide controls. In previous studies, data-driven approaches have been employed to model shallow landslides on a regional scale, focusing on analyzing the spatial aspects and time-varying conditions separately. Still, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging. This study aims to predict the occurrence of precipitation-induced shallow landslides in space and time within the Italian province of South Tyrol (7,400 km²). In this context, we investigate the benefits of considering precipitation leading to landslide events as a functional predictor, in contrast to conventional approaches that treat precipitation as a scalar predictor. We built upon hourly precipitation analysis data and past landslide occurrences from 2012 to 2021. We implemented a novel functional generalized additive model to establish statistical relationships between the spatiotemporal occurrence of shallow landslides, various static factors included as scalar predictors, and the hourly precipitation pattern preceding a potential landslide used as a functional predictor. We evaluated the resulting predictions through several cross-validation routines, achieving high model performance scores. To showcase the model capabilities, we performed a hindcast for the storm event in the Passeier Valley on August 4th and 5th, 2016. This novel approach enables the prediction of landslides in space and time for large areas by accounting for static and dynamic functional landslide controls, seasonal effects, statistical uncertainty, and underlying data limitations.
AB - Shallow landslides are geomorphic hazards in mountainous terrains across the globe. Their occurrence can be attributed to the interplay of static and dynamic landslide controls. In previous studies, data-driven approaches have been employed to model shallow landslides on a regional scale, focusing on analyzing the spatial aspects and time-varying conditions separately. Still, the joint assessment of shallow landslides in space and time using data-driven methods remains challenging. This study aims to predict the occurrence of precipitation-induced shallow landslides in space and time within the Italian province of South Tyrol (7,400 km²). In this context, we investigate the benefits of considering precipitation leading to landslide events as a functional predictor, in contrast to conventional approaches that treat precipitation as a scalar predictor. We built upon hourly precipitation analysis data and past landslide occurrences from 2012 to 2021. We implemented a novel functional generalized additive model to establish statistical relationships between the spatiotemporal occurrence of shallow landslides, various static factors included as scalar predictors, and the hourly precipitation pattern preceding a potential landslide used as a functional predictor. We evaluated the resulting predictions through several cross-validation routines, achieving high model performance scores. To showcase the model capabilities, we performed a hindcast for the storm event in the Passeier Valley on August 4th and 5th, 2016. This novel approach enables the prediction of landslides in space and time for large areas by accounting for static and dynamic functional landslide controls, seasonal effects, statistical uncertainty, and underlying data limitations.
KW - Space-time modeling
KW - FGAMs
KW - Functional predictors
KW - Precipitation time series
KW - INCA
U2 - 10.31223/X5VB0M
DO - 10.31223/X5VB0M
M3 - Preprint
SP - 1
EP - 35
BT - Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy
PB - Earth ArXiv
ER -