Assessment of data availability for the development of landslide fatality curves

Grahn, Tonje; Jaldell, Henrik

doi:10.1007/s10346-016-0775-6

Cited by 31 publications

(7 citation statements)

References 33 publications

(74 reference statements)

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“…Landslides are ubiquitous in the hills, mountains, and high coasts that constellate the landmasses (Guzzetti et al, 2012), and in many areas they cause significant human, societal, economic, and environmental damage and costs (Brabb, 1989(Brabb, , 1991Nadim et al, 2006;Dowling and Santi, 2014;Badoux et al, 2016;Grahn and Jaldell, 2017;Kirschbaum et al, 2009;Pereira et al, 2017;Froude and Petley, 2018;Salvati et al, 2018;Rossi et al, 2019). The reliable anticipation of landslides and their consequences is thus of primary importance.…”

Section: Introductionmentioning

confidence: 99%

Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

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Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete "point" events in the landscape, we propose a novel Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components: (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide "intensity", i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide "susceptibility"); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of "unobserved" latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km 2 area into 889 slope units (SUs). In each SU, we computed the percentage of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model, and 13 lithological and bedding attitude covariates obtained from a 1:10,000 scale geological map. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our "baseline" model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to a "traditional" landslide susceptibility model. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In partic...

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Section: Introductionmentioning

confidence: 99%

Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

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“…As one of the most frequently-occurring geological disasters in the world, landslides have triggered a series of threats to human society including casualties, economic losses, infrastructure destruction, and geological environment problems [1][2][3]. Therefore, to reduce the losses, it is absolutely necessary to study the landslide susceptibility in a region [4,5].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Wang

2019

Symmetry

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The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.Symmetry 2019, 11, 762 2 of 24years, machine learning method has been gradually applied in landslide susceptibility mapping researches, such as artificial neural network (ANN) [17][18][19], support vector machine (SVM) [20][21][22], logistic model tree (LMT) [23,24], rotation forest (RF) [25,26], classification and regression tree (CART) [27,28], adaptive neuro-fuzzy inference systems (ANFIS) [29,30], and genetic algorithm (GA) [31,32]. Furthermore, statistical approach is another widely-used model which can also be divided into two types: bivariate and multivariate. In statistical approaches, the weight of each class of every factor was calculated by overlying the landslide inventory map and landslide conditioning factors map [33,34]. The frequently-used statistical approaches are frequency ratio (FR) [35][36][37], logistic regression (LR), evidential belief function (EBF) [38,39], weights of evidence (WoE) [40,41], certainty factor (CF) [42,43], and information value (IV) [44,45].In this paper, three bivariate statistical methods, namely weight of evidence (WoE), evidence belief function (EBF), index of entropy (IoE), coupled with logistic regression (LR) were introduced to carry out the landslide susceptibility mapping in Muchuan County, which lies in the southeast of Sichuan Province, China. All the approaches were first applied in Muchuan County and the main purpose of this article is to obtain the landslide susceptibility maps through the above models. The main difference between this study and literatures referenced above is to achieve accurate landslide susceptibility maps for...

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“…3 According to the statistics from the Centre for Research on Epidemiology of Disasters, 17% of fatalities are the consequences of landslides. 4,5 Landslide is said to be a complex phenomenon influenced by the multifaceted causative parameters that include rainfall, weathering, seismic activity, movements in the slop, and so on, which result in the movement of debris (also termed as mudslides or mudflows), mass of a rock, or earth down a slop. 6 In India, the heavy rainfall due to the southwest monsoon is continuously causing floods and landslides in its various mountainous regions.…”

Section: Overview and Backgroundmentioning

confidence: 99%

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

Menon¹,

Verma

Kaur

et al. 2021

Big Data

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An exponential progression in the miniaturization of communicating devices has proliferated the generation of a large volume of data termed as ''big data.'' The technological advancements in the micro-electro/mechanical system has made it possible to design the low-cost, low-power consuming artificial intelligence (AI)-based wireless sensor nodes to gather the big data belonging to various attributes from their surroundings. These nodes help in the early detection and prediction for the occurrence of landslides, which are among the catastrophic hazards. A profusion of research has focused on exploiting the potential of sensors for continuous monitoring and detecting the landslides at the earliest. However, the limited energy resources of sensor nodes give rise to the huge challenge for the network longevity pertaining to landslide detection. To address this concern, in this article, we propose an optimized routing and big data gathering system for landslide detection using (AI)-based wireless sensor network (WSN) (ORLAW). Since we propose a distributed routing mechanism, AI has a major role to play in the intelligent detection of landslides that too without the intervention of an external entity. We use the Dynamic Salp Swarm Algorithm for the cluster head selection in ORLAW. Two data collecting sinks are deployed on the opposite sides of the network, which is assumed to be a mountainous area. It is discerned from the simulation examination that ORLAW elongates the reliability period by 23.9% compared with the recently proposed clusterbased intelligent routing protocol, and also outperforms many others in the perspective of energy efficient management of big data.

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Assessment of data availability for the development of landslide fatality curves

Cited by 31 publications

References 33 publications

Space-Time Landslide Predictive Modelling

Space-Time Landslide Predictive Modelling

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection

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