Description of a GIS-based rockfall hazard assessment methodology and its application in mountainous sites

Depountis, Nikolaos; Nikolakopoulos, Konstantinos G.; Kavoura, Katerina; Sabatakakis, N.

doi:10.1007/s10064-019-01590-3

Cited by 21 publications

(9 citation statements)

References 43 publications

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“…Each factor used in the assessments must be statistically demonstrated to affect the spatial distribution of the process; to do this, the chi-squared test was applied in this research. This test is based on comparing the observed and expected frequencies of the studied phenomenon (Davis 1986). An example of its application can be found in a paper published by Komac (2012).…”

Section: Zonation Methodologymentioning

confidence: 99%

Identification of rockfall source areas using the seed cell concept and bivariate susceptibility modelling

Toševski

Pollak

Perković

2021

Bull Eng Geol Environ

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The objective of this research was to prepare a rockfall susceptibility map. Explorations were conducted in the Dubračina River basin (Croatia). The input data included a geological map, an orthophoto and a 1-m digital terrain model (DTM). After a talus inventory was prepared, the seed cell concept was applied to define the rockfall source areas. The contributing factors (predictors) of rockfalls were evaluated by the chi-squared test. The analysis confirmed the following predictors: CORINE land cover, lithology, slope, aspect, distance from a spring, distance from a road, distance from a fault, distance from a stream, and distance from the rock-soil geological boundary. A matrix pairwise comparison of the predictor ratings was used to define the most significant contributing factors. The predictors that affected the susceptibility map in the share of 86.3% were the slope (61.6%), lithology (13.4%), CORINE land cover (6.2%), and distance from the rock-soil geological boundary (5.1%). Two susceptibility maps were prepared: one using all nine contributing factors and another using the four most significant factors. The analysis showed that both maps were good, with the same areas under the receiver operating characteristic (ROC) curves. The map prepared with only four contributing factors can be considered a better map due to its more precise spatial definition of critical areas. It can be concluded that geological map, 1-m DTM and orthophoto provide enough data to prepare reliable rockfall susceptibility map. The application of the bivariate statistical zonation method called the “frequency ratio method” was proven to be successful. This research demonstrates that the application of the seed cell concept can be useful to speed up the process of rockfall source area detections in large research regions.

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Section: Zonation Methodologymentioning

confidence: 99%

Identification of rockfall source areas using the seed cell concept and bivariate susceptibility modelling

Toševski

Pollak

Perković

2021

Bull Eng Geol Environ

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“…Remote sensing (RS)-based data and Geographic Information System (GIS) tools, together with machine-learning approaches, have been popularly used in numerous studies. In recent studies, soft computing, heuristic, statistic, and deterministic models have been used to evaluate rockfall in terms of source identification [8][9][10], kinematic modeling [11][12][13], hazard assessment [1,14,15], and risk assessment [16][17][18]. The numerical modeling predicts either the blocks trajectories using Newtonian mechanics or the rockfall runout zone based on empirical measurements [2].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based and 3D Kinematic Models for Rockfall Hazard Assessment Using LiDAR Data and GIS

et al. 2020

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Rockfall is one of the most hazardous phenomena in mountainous and hilly regions with high and steep terrain. Such incidents can cause massive damage to people, properties, and infrastructure. Therefore, proper rockfall hazard assessment methods are required to save lives and provide a guide for the development of an area. The aim of this research is to develop a method for rockfall hazard assessment at two different scales (regional and local). A high-resolution airborne laser scanning (ALS) technique was utilized to derive an accurate digital terrain model (DTM); next, a terrestrial laser scanner (TLS) was used to capture the topography of the two most critical areas within the study area. A staking machine-learning model based on different classifiers, namely logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (KNN), was optimized and employed to determine rockfall probability by utilizing various rockfall conditioning factors. A developed 3D rockfall kinematic model was used to obtain rockfall trajectories, velocity, frequency, bouncing height, kinetic energy, and impact location. Next, a spatial model combined with a fuzzy analytical hierarchy process (fuzzy-AHP) integrated in the Geographic Information System (GIS) was developed to assess rockfall hazard in two different areas in Ipoh, Malaysia. Additionally, mitigation processes were suggested and assessed to provide a comprehensive information for urban planning management. The results show that, the stacking random forest–k-nearest neighbor (RF-KNN) model is the best hybrid model compared to other tested models with an accuracy of 89%, 86%, and 87% based on training, validation, and cross-validation datasets, respectively. The three-dimension rockfall kinematic model was calibrated with an accuracy of 93% and 95% for the two study areas and subsequently the rockfall trajectories and their characteristics were derived. The assessment of the suggested mitigation processes proves that the proposed methods can reduce or eliminate rockfall hazard in these areas. According to the results, the proposed method can be generalized and applied in other geographical places to provide decision-makers with a comprehensive rockfall hazard assessment.

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“…To this end, it must be coupled with simplicity. In this sense, another important common characteristic found in recent related literature is the use of 3D numerical models and simulations to account for the kinematics and trajectories of rocks [20]- [23]. However, these models are often complex, demanding in computational terms and difficult to reconcile with spatial tools such as GIS.…”

mentioning

confidence: 99%

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

Jato-Espino

Roldán-Valcarce

Collazos-Arias³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Rockfall events are one of the most frequent types of mass wasting in mountainous areas, causing service and traffic disruption, as well as infrastructure and human damage. Hence, having accurate tools to model these hazards becomes crucial to prevent fatalities, especially in a context of Climate Change whereby the effects of these phenomena might be exacerbated. Under this premise, this research concerned the development of a framework for assessing rockfall hazard in mountainous areas. First, a set of factors expected to favour rockfalls were processed and aggregated using spatial analysis tools, yielding a series of hazard maps with which to fit observed data through statistical modelling. The validation process was undertaken with the support of a database containing the number of rocks removed from a mountainous road section located in Cantabria, northern Spain. The results achieved, which demonstrated the accuracy of the proposed approach to reproduce rockfall hazard using frequency data, highlighted the primary role played by factors such as slope, runoff threshold and precipitation to explain the occurrence of these events. The effects of Climate Change were considerably influenced by the fluctuations in the projections of precipitation, which limited the variations in the spatial distribution and magnitude of rockfall hazard.

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Description of a GIS-based rockfall hazard assessment methodology and its application in mountainous sites

Cited by 21 publications

References 43 publications

Identification of rockfall source areas using the seed cell concept and bivariate susceptibility modelling

Identification of rockfall source areas using the seed cell concept and bivariate susceptibility modelling

Machine Learning-Based and 3D Kinematic Models for Rockfall Hazard Assessment Using LiDAR Data and GIS

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

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