Comparative Analysis of Deterministic and Semiquantitative Approaches for Shallow Landslide Risk Modeling in Rwanda

Risk Analysis ◽  
2019 ◽  
Vol 39 (11) ◽  
pp. 2576-2595 ◽  
Author(s):  
Jean Baptiste Nsengiyumva ◽  
Geping Luo ◽  
Egide Hakorimana ◽  
Richard Mind'je ◽  
Aboubakar Gasirabo ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Shallow Landslide ◽  
Landslide Risk ◽  
Risk Modeling

Cost-based analysis of mitigation measures for shallow-landslide risk reduction strategies

2016 ◽  
Vol 213 ◽  
pp. 142-157 ◽  
Author(s):  
Jorge Pedro Galve ◽  
Andrea Cevasco ◽  
Pierluigi Brandolini ◽  
Daniela Piacentini ◽  
José Miguel Azañón ◽  
...  
Keyword(s):  
Risk Reduction ◽  
Shallow Landslide ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Reduction Strategies ◽  
Risk Reduction Strategies ◽  
Landslide Risk Reduction

scholarly journals Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

2021 ◽  
Vol 9 ◽  
Author(s):  
Penghui Ou ◽  
Weicheng Wu ◽  
Yaozu Qin ◽  
Xiaoting Zhou ◽  
Wenchao Huangfu ◽  
...  
Keyword(s):  
Regional Scale ◽  
Binary Logistic Regression ◽  
Technical Support ◽  
Landslide Hazard ◽  
Information Value ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Risk Modeling ◽  
Binary Logistic Regression Model ◽  
Linear Features

Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.

Assessment of shallow landslide risk mitigation measures based on land use planning through probabilistic modelling

Landslides ◽  
2014 ◽  
Vol 12 (1) ◽  
pp. 101-114 ◽  
Author(s):  
Jorge Pedro Galve ◽  
Andrea Cevasco ◽  
Pierluigi Brandolini ◽  
Mauro Soldati
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Risk Mitigation ◽  
Shallow Landslide ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Probabilistic Modelling

scholarly journals Shallow landslide prediction and analysis with risk assessment using a spatial model in the coastal region in the state of São Paulo, Brazil

2013 ◽  
Vol 1 (5) ◽  
pp. 5199-5236
Author(s):  
P. I. M. Camarinha ◽  
V. Canavesi ◽  
R. C. S. Alvalá
Keyword(s):  
Risk Assessment ◽  
Spatial Data ◽  
Urban Areas ◽  
Coastal Region ◽  
Spatial Prediction ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Serra Do Mar

Abstract. In Brazil, most of the disasters involving landslide occur in coastal regions, with population density concentrated on steep slopes. Thus, different approaches have been used to evaluate the landslide risk, although the greatest difficulty is related to the scarcity of spatial data with good quality. In this context, four cities located on the southeast coast of Brazil – Santos, Cubatão, Caraguatatuba and Ubatuba – in a region with the rough reliefs of the Serra do Mar and with a history of natural disasters were evaluated. Spatial prediction by fuzzy gamma technique was used for the landslide susceptibility mapping, considering environmental variables from data and software in the public domain. To validate the susceptibility mapping results, it was overlapped with risk sectors provided by the Geological Survey of Brazil (CPRM). A positive correlation was observed between the classes most susceptible and the location of these sectors. The results were also analyzed from the categorization of risk levels provided by CPRM. To compare the approach with other studies using landslide-scar maps, correlated indexes were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas and can be replicated to municipalities that do not have risk areas mapped.

scholarly journals Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China

2020 ◽  
Vol 9 (11) ◽  
pp. 695
Author(s):  
Yang Zhang ◽  
Weicheng Wu ◽  
Yaozu Qin ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
...  
Keyword(s):  
Human Life ◽  
Landslide Hazard ◽  
Landslide Risk ◽  
Disaster Prevention ◽  
Risk Modeling ◽  
Prevention Measures ◽  
Training Set ◽  
Hazard Risk ◽  
Calculated Probability ◽  
Validation Set

Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

scholarly journals Rainfall-Induced Shallow Landslide Detachment, Transit and Runout Susceptibility Mapping by Integrating Machine Learning Techniques and GIS-Based Approaches

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 488 ◽  
Author(s):  
Mariano Di Napoli ◽  
Diego Di Martire ◽  
Giuseppe Bausilio ◽  
Domenico Calcaterra ◽  
Pierluigi Confuorto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Geographical Information ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Susceptibility Map ◽  
Learning Techniques

Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.

scholarly journals Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China

2021 ◽  
Vol 13 (9) ◽  
pp. 4830
Author(s):  
Wenchao Huangfu ◽  
Weicheng Wu ◽  
Xiaoting Zhou ◽  
Ziyu Lin ◽  
Guiliang Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Landslide Risk ◽  
Risk Modeling ◽  
Prevention Measures ◽  
Training Set ◽  
Reliable Prediction ◽  
Landslide Occurrence ◽  
Hazard Risk ◽  
Causative Factors ◽  
Validation Set

Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.

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