4.7 Article

Use of ROC curves for early warning of landslide displacement rates in response to precipitation (Piagneto landslide, Northern Apennines, Italy)

期刊

LANDSLIDES
卷 14, 期 3, 页码 1241-1252

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10346-016-0781-8

关键词

ROC curves; Landslides; Displacement rate; Cumulated precipitation; Early warning; Northern Apennines

资金

  1. Civil Protection Agency of the Emilia-Romagna Region (ASPER-RER)

向作者/读者索取更多资源

Active landslides are generally characterized by variations in displacement rate in response to cumulated precipitation. Velocities that are only exceeded in a limited number of days during the year might be considered as critical events, since they might determine, or prelude to, a significant evolution of the landslide. The purpose of this paper is to present a novel approach based on the use of receiver operating characteristic (ROC) curves for assessing cumulated precipitation thresholds that can provide early warning for the occurrence of critical events such as the exceedance of rare displacement rates. The approach has been developed and tested in the Piagneto landslide, an active complex rock slide-debris slide in the Northern Apennines of Italy, for which a 5-year continuous surveying monitoring dataset is available. On the basis of the first 4 years of monitoring data (training dataset), threshold curves relating cumulative precipitation (mm) to precipitation moving windows (days) have been generated by using different benchmarks that, in literature, are used to estimate the maximum predictive performance of ROC curves. These threshold curves have been successfully validated using the last 1 year of monitoring data (validation dataset). They have then been used to simulate how they might help defining different early warning levels in due advance. The proposed methodology can be replicated in any landslide for which a monitoring dataset that includes recurrent acceleration events in response to precipitation is available.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Engineering, Geological

Redundancy and coherence of multi-method displacement monitoring data as key issues for the analysis of extremely slow landslides (Isarco valley, Eastern Alps, Italy)

Lucia Simeoni, Francesco Ronchetti, Carlo Costa, Paolo Joris, Alessandro Corsini

ENGINEERING GEOLOGY (2020)

Article Geography, Physical

Debris flows rainfall thresholds in the Apennines of Emilia-Romagna (Italy) derived by the analysis of recent severe rainstorms events and regional meteorological data

Giuseppe Ciccarese, Marco Mulas, Pier Paolo Alberoni, Giovanni Truffelli, Alessandro Corsini

GEOMORPHOLOGY (2020)

Article Environmental Sciences

Integration of Digital Image Correlation of Sentinel-2 Data and Continuous GNSS for Long-Term Slope Movements Monitoring in Moderately Rapid Landslides

Marco Mulas, Giuseppe Ciccarese, Giovanni Truffelli, Alessandro Corsini

REMOTE SENSING (2020)

Article Engineering, Geological

Tracer test to assess flow and transport parameters of an earth slide: The Montecagno landslide case study (Italy)

Francesco Ronchetti, Leonardo Piccinini, Manuela Deiana, Giuseppe Ciccarese, Valentina Vincenzi, Alessandro Aguzzoli, Gianluca Malavasi, Paolo Fabbri, Alessandro Corsini

ENGINEERING GEOLOGY (2020)

Article Engineering, Geological

Combining spatial modelling and regionalization of rainfall thresholds for debris flows hazard mapping in the Emilia-Romagna Apennines (Italy)

G. Ciccarese, M. Mulas, A. Corsini

Summary: The study combines spatial modeling and regionalization of debris rainfall thresholds to assess and map debris flow initiation hazard in the Emilia-Romagna Apennines, Italy. Different spatial statistical models were trained and compared, resulting in a hazard map that classifies areas into high, medium, low, or null hazard based on spatial and temporal probabilities. This map is considered reliable for integrating existing inventory maps in land-use regulation and emergency planning despite its limitations.

LANDSLIDES (2021)

Article Environmental Sciences

Multidisciplinary non-invasive investigations to develop a hydrogeological conceptual model supporting slope kinematics at Fontana Cornia landslide, Northern Apennines, Italy

Alessandro Aguzzoli, Diego Arosio, Marco Mulas, Giuseppe Ciccarese, Benedikt Bayer, Gerfried Winkler, Francesco Ronchetti

Summary: A multidisciplinary approach was used to define the hydrogeological conceptual model of the complex Fontana Cornia landslide in Italy. The results of seismic refraction tomography and electrical resistivity tomography investigations show the presence of a curvilinear sliding surface and undulations that can store water. The joint interpretation of geophysical outcomes, hydrogeological analyses, and spring measurements allowed the identification of specific hydrologic stages and the development of a hydrogeological model that can explain the landslide displacements detected with In-SAR monitoring.

ENVIRONMENTAL EARTH SCIENCES (2022)

Article Environmental Sciences

Detecting Recent Dynamics in Large-Scale Landslides via the Digital Image Correlation of Airborne Optic and LiDAR Datasets: Test Sites in South Tyrol (Italy)

Melissa Tondo, Marco Mulas, Giuseppe Ciccarese, Gianluca Marcato, Giulia Bossi, David Tonidandel, Volkmar Mair, Alessandro Corsini

Summary: Large-scale slow-moving deep-seated landslides are complex and potentially highly damaging phenomena. In this study, multi-temporal airborne optic and LiDAR surveys were used to detect and quantify slope movements. Two DIC algorithms, NCC and PC, were applied to the datasets, and although they struggled to quantify sub-pixel displacement patterns in densely vegetated areas, they successfully differentiated stable and active parts of the slopes, providing valuable information for risk management.

REMOTE SENSING (2023)

Article Computer Science, Information Systems

Displacements of an Active Moderately Rapid Landslide-A Dataset Retrieved by Continuous GNSS Arrays

Marco Mulas, Giuseppe Ciccarese, Giovanni Truffelli, Alessandro Corsini

暂无数据