Rainfall thresholds for the possible occurrence of landslides in Italy

Abstract. In Italy, rainfall is the primary trigger of landslides that frequently cause fatalities and large economic damage. Using a variety of information sources, we have compiled a catalogue listing 753 rainfall events that have resulted in landslides in Italy. For each event in the catalogue, the exact or approximate location of the landslide and the time or period of initiation of the slope failure is known, together with information on the rainfall duration D, and the rainfall mean intensity I, that have resulted in the slope failure. The catalogue represents the single largest collection of information on rainfall-induced landslides in Italy, and was exploited to determine the minimum rainfall conditions necessary for landslide occurrence in Italy, and in the Abruzzo Region, central Italy. For the purpose, new national rainfall thresholds for Italy and new regional rainfall thresholds for the Abruzzo Region were established, using two independent statistical methods, including a Bayesian inference method and a new Frequentist approach. The two methods proved complementary, with the Bayesian method more suited to analyze small data sets, and the Frequentist method performing better when applied to large data sets. The new regional thresholds for the Abruzzo Region are lower than the new national thresholds for Italy, and lower than the regional thresholds proposed in the literature for the Piedmont and Lombardy Regions in northern Italy, and for the Campania Region in southern Italy. This is important, because it shows that landslides in Italy can be triggered by less severe rainfall conditions than previously recognized. The Frequentist method experimented in this work allows for the definition of multiple minimum rainfall thresholds, each based on a different exceedance probability level. This makes the thresholds suited for the design of probabilistic schemes for the prediction of rainfall-induced landslides. A scheme based on four probabilistic thresholds is proposed. The four thresholds separate five fields, each characterized by different rainfall intensity-duration conditions, and corresponding different probability of possible landslide occurrence. The scheme can be implemented in landslide warning systems that operate on rainfall thresholds, and on precipitation measurements or forecasts.

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Rainfall thresholds for shallow landslide occurrence in Calabria, southern Italy

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-317-2014 ◽

2014 ◽

Vol 14 (2) ◽

pp. 317-330 ◽

Cited By ~ 70

Author(s):

C. Vennari ◽

S. L. Gariano ◽

L. Antronico ◽

M. T. Brunetti ◽

G. Iovine ◽

...

Keyword(s):

Southern Italy ◽

Central Italy ◽

Mediterranean Area ◽

Shallow Landslide ◽

Google Earth ◽

Shallow Landslides ◽

Rainfall Thresholds ◽

Rainfall Events ◽

Landslide Occurrence ◽

Hourly Rainfall

Abstract. In many areas, rainfall is the primary trigger of landslides. Determining the rainfall conditions responsible for landslide occurrence is important, and may contribute to saving lives and properties. In a long-term national project for the definition of rainfall thresholds for possible landslide occurrence in Italy, we compiled a catalogue of 186 rainfall events that resulted in 251 shallow landslides in Calabria, southern Italy, from January 1996 to September 2011. Landslides were located geographically using Google Earth®, and were given a mapping and a temporal accuracy. We used the landslide information, and sub-hourly rainfall measurements obtained from two complementary networks of rain gauges, to determine cumulated event vs. rainfall duration (ED) thresholds for Calabria. For this purpose, we adopted an existing method used to prepare rainfall thresholds and to estimate their associated uncertainties in central Italy. The regional thresholds for Calabria were found to be nearly identical to previous ED thresholds for Calabria obtained using a reduced set of landslide information, and slightly higher than the ED thresholds obtained for central Italy. We segmented the regional catalogue of rainfall events with landslides in Calabria into lithology, soil regions, rainfall zones, and seasonal periods. The number of events in each subdivision was insufficient to determine reliable thresholds, but allowed for preliminary conclusions about the role of the environmental factors in the rainfall conditions responsible for shallow landslides in Calabria. We further segmented the regional catalogue based on administrative subdivisions used for hydro-meteorological monitoring and operational flood forecasting, and we determined separate ED thresholds for the Tyrrhenian and the Ionian coasts of Calabria. We expect the ED rainfall thresholds for Calabria to be used in regional and national landslide warning systems. The thresholds can also be used for landslide hazard and risk assessments, and for erosion and landscape evolution studies, in the study area and in similar physiographic regions in the Mediterranean area.

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Rainfall thresholds for shallow landslides occurrence in Calabria, southern Italy

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-1-5141-2013 ◽

2013 ◽

Vol 1 (5) ◽

pp. 5141-5179 ◽

Cited By ~ 1

Author(s):

C. Vennari ◽

S. L. Gariano ◽

L. Antronico ◽

M. T. Brunetti ◽

G. Iovine ◽

...

Keyword(s):

Southern Italy ◽

Central Italy ◽

Warning System ◽

Mediterranean Area ◽

Google Earth ◽

Shallow Landslides ◽

Rainfall Thresholds ◽

Rainfall Events ◽

Landslide Occurrence ◽

Hourly Rainfall

Abstract. In many areas, rainfall is the primary trigger of landslides. Determining the rainfall conditions responsible for landslide occurrence is important, and may contribute to save lives and properties. In a long-term national project for the definition of rainfall thresholds for possible landslide occurrence in Italy, and for the implementation of a national landslide warning system, we compiled a catalogue of 186 rainfall events that have resulted in 251 shallow landslides in Calabria, southern Italy, from January 1996 to September 2011. Landslides were located geographically using Google Earth®, and were given a mapping and a temporal accuracy. We used the landslide information, and sub-hourly rainfall measurements obtained from two complementary networks of rain gauges, to determine cumulated event vs. rainfall duration (ED) thresholds for Calabria. For the purpose, we adopted an existing method used to prepare rainfall thresholds and to estimate their associated uncertainties in central Italy. The regional thresholds for Calabria were found nearly identical to previous ED thresholds for Calabria obtained using a reduced set of landslide information, and slightly higher than the ED thresholds obtained for central Italy. We segmented the regional catalogue of rainfall events with landslides on lithology, soil regions, rainfall zones, and seasonal periods. The number of events in each subdivision was insufficient to determine reliable thresholds, but allowed for preliminary conclusions on the role of the environmental factors on the rainfall conditions responsible for shallow landslides in Calabria. We further segmented the regional catalogue based on administrative subdivisions used for hydro-meteorological monitoring and operational flood forecasting, and we determined separate ED thresholds for the Tyrrhenian and the Ionian coasts of Calabria. We expect the ED rainfall thresholds for Calabria to be used in regional and national landslide warning systems. The thresholds can also be used for landslide hazard and risk assessments, and for erosion and landscape evolution studies, in the study area and in similar physiographic regions in the Mediterranean area.

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Relationships between Morphostructural/Geological Framework and Landslide Types: Historical Landslides in the Hilly Piedmont Area of Abruzzo Region (Central Italy)

Land ◽

10.3390/land10030287 ◽

2021 ◽

Vol 10 (3) ◽

pp. 287

Author(s):

Gianluca Esposito ◽

Cristiano Carabella ◽

Giorgio Paglia ◽

Enrico Miccadei

Keyword(s):

Spatial Scales ◽

Central Italy ◽

Integrated Approach ◽

Natural Phenomenon ◽

Inventory Analysis ◽

Landslide Occurrence ◽

Territorial Planning ◽

Abruzzo Region ◽

Slope Instabilities ◽

Geomorphological Evolution

Landslides are a widespread natural phenomenon that play an important role in landscape evolution and are responsible for several casualties and damages. The Abruzzo Region (Central Italy) is largely affected by different types of landslides from mountainous to coastal areas. In particular, the hilly piedmont area is characterized by active geomorphological processes, mostly represented by slope instabilities related to mechanisms and factors that control their evolution in different physiographic and geological–structural conditions. This paper focuses on the detailed analysis of three selected case studies to highlight the multitemporal geomorphological evolution of landslide phenomena. An analysis of historical landslides was performed through an integrated approach combining literature data and landslide inventory analysis, relationships between landslide types and lithological units, detailed photogeological analysis, and geomorphological field mapping. This analysis highlights the role of morphostructural features on landslide occurrence and distribution and their interplay with the geomorphological evolution. This work gives a contribution to the location, abundance, activity, and frequency of landslides for the understanding of the spatial interrelationship of landslide types, morphostructural setting, and climate regime in the study area. Finally, it represents a scientific tool in geomorphological studies for landslide hazard assessment at different spatial scales, readily available to interested stakeholders to support sustainable territorial planning.

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A susceptibility-based rainfall threshold approach for landslide occurrence

Natural Hazards and Earth System Science ◽

10.5194/nhess-19-775-2019 ◽

2019 ◽

Vol 19 (4) ◽

pp. 775-789 ◽

Cited By ~ 12

Author(s):

Elise Monsieurs ◽

Olivier Dewitte ◽

Alain Demoulin

Keyword(s):

Landslide Susceptibility ◽

Rainfall Threshold ◽

Exceedance Probability ◽

Equal Weight ◽

Antecedent Rainfall ◽

Rainfall Thresholds ◽

Landslide Occurrence ◽

Continental Scale ◽

Satellite Rainfall ◽

Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

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Multipronged dental analyses reveal dietary differences in last foragers and first farmers at Grotta Continenza, central Italy (15,500–7000 BP)

Scientific Reports ◽

10.1038/s41598-021-82401-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alessia Nava ◽

Elena Fiorin ◽

Andrea Zupancich ◽

Marialetizia Carra ◽

Claudio Ottoni ◽

...

Keyword(s):

Dental Plaque ◽

Central Italy ◽

Abruzzo Region ◽

Nuanced Understanding ◽

Fucino Basin ◽

Changes Over Time ◽

Diachronic Changes ◽

Dental Conditions ◽

Over Time ◽

Late Palaeolithic

AbstractThis paper provides results from a suite of analyses made on human dental material from the Late Palaeolithic to Neolithic strata of the cave site of Grotta Continenza situated in the Fucino Basin of the Abruzzo region of central Italy. The available human remains from this site provide a unique possibility to study ways in which forager versus farmer lifeways affected human odonto-skeletal remains. The main aim of our study is to understand palaeodietary patterns and their changes over time as reflected in teeth. These analyses involve a review of metrics and oral pathologies, micro-fossils preserved in the mineralized dental plaque, macrowear, and buccal microwear. Our results suggest that these complementary approaches support the assumption about a critical change in dental conditions and status with the introduction of Neolithic foodstuff and habits. However, we warn that different methodologies applied here provide data at different scales of resolution for detecting such changes and a multipronged approach to the study of dental collections is needed for a more comprehensive and nuanced understanding of diachronic changes.

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Classification of jujube defects in small data sets based on transfer learning

Neural Computing and Applications ◽

10.1007/s00521-021-05715-2 ◽

2021 ◽

Author(s):

Jianping Ju ◽

Hong Zheng ◽

Xiaohang Xu ◽

Zhongyuan Guo ◽

Zhaohui Zheng ◽

...

Keyword(s):

Transfer Learning ◽

Loss Function ◽

Training Model ◽

Parameter Distribution ◽

Test Accuracy ◽

Small Data ◽

Data Sets ◽

Data Set ◽

Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

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45 A permutation test for validation of genomic estimated breeding values

Journal of Animal Science ◽

10.1093/jas/skaa278.016 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 8-9

Author(s):

Zahra Karimi ◽

Brian Sullivan ◽

Mohsen Jafarikia

Keyword(s):

Permutation Test ◽

Breeding Value ◽

Small Data ◽

Data Sets ◽

Type I ◽

Future Performance ◽

Small Data Sets ◽

Estimated Breeding Value ◽

Estimated Breeding Values ◽

Top 40

Abstract Previous studies have shown that the accuracy of Genomic Estimated Breeding Value (GEBV) as a predictor of future performance is higher than the traditional Estimated Breeding Value (EBV). The purpose of this study was to estimate the potential advantage of selection on GEBV for litter size (LS) compared to selection on EBV in the Canadian swine dam line breeds. The study included 236 Landrace and 210 Yorkshire gilts born in 2017 which had their first farrowing after 2017. GEBV and EBV for LS were calculated with data that was available at the end of 2017 (GEBV2017 and EBV2017, respectively). De-regressed EBV for LS in July 2019 (dEBV2019) was used as an adjusted phenotype. The average dEBV2019 for the top 40% of sows based on GEBV2017 was compared to the average dEBV2019 for the top 40% of sows based on EBV2017. The standard error of the estimated difference for each breed was estimated by comparing the average dEBV2019 for repeated random samples of two sets of 40% of the gilts. In comparison to the top 40% ranked based on EBV2017, ranking based on GEBV2017 resulted in an extra 0.45 (±0.29) and 0.37 (±0.25) piglets born per litter in Landrace and Yorkshire replacement gilts, respectively. The estimated Type I errors of the GEBV2017 gain over EBV2017 were 6% and 7% in Landrace and Yorkshire, respectively. Considering selection of both replacement boars and replacement gilts using GEBV instead of EBV can translate into increased annual genetic gain of 0.3 extra piglets per litter, which would more than double the rate of gain observed from typical EBV based selection. The permutation test for validation used in this study appears effective with relatively small data sets and could be applied to other traits, other species and other prediction methods.

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A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

Design Engineering, Parts A and B ◽

10.1115/imece2005-80029 ◽

2005 ◽

Cited By ~ 10

Author(s):

Jungeui Hong ◽

Elizabeth A. Cudney ◽

Genichi Taguchi ◽

Rajesh Jugulum ◽

Kioumars Paryani ◽

...

Keyword(s):

Neural Network ◽

Small Data ◽

Data Sets ◽

Comparison Study ◽

Data Set ◽

Set Size ◽

Breast Cancer Study ◽

Discriminant Ability ◽

Small Data Sets ◽

Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

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