Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea

Oh, Hyun-Joo; Lee, Saro

doi:10.1007/s12665-010-0864-0

Cited by 39 publications

(16 citation statements)

References 42 publications

(38 reference statements)

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“…The percentage of landslide occurrence (POLO) and its cumulative (CPOLO) are commonly used as a good indicator to measure the performance of LSM: The steeper the CPOLO curve, the better the capability of LSM to predict landslides and to validate LSMs (e.g., Chung and Fabbri 2003;Frattini et al 2010;Mezughi et al 2011;Oh and Lee 2011). We follow this procedure to derive a success rate curve by calculating the LSM of all cells using Eq.…”

Section: Resultsmentioning

confidence: 99%

A new region-based preparatory factor for landslide susceptibility models: the total flux

Liu

Luo

Chen³

et al. 2015

Landslides

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This paper presents a new region-based preparatory factor, total flux (TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides.

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Section: Resultsmentioning

confidence: 99%

A new region-based preparatory factor for landslide susceptibility models: the total flux

Liu

Luo

Chen³

et al. 2015

Landslides

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“…Therefore, we applied the principle of conditional probability and selected an FR model to determine the spatial relationship between flooded areas and flood-related factors. We subdivided the data layer into classes within each study area, based on the percentage of the total area that was flooded [54]. In Equation (6), P(P) represents the area ratio of a class or type to a given number of unit cells, including the domain fraction of the class; P(O) is the percentage of flood occurrence within the class [55].…”

Section: Methodsmentioning

confidence: 99%

Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools

Lee

et al. 2018

Sustainability

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Using geographic information system (GIS) tools and data-mining models, this study analyzed the relationships between flood areas and correlated hydrological factors to map the regional flood susceptibility of the Seoul metropolitan area in South Korea. We created a spatial database of data describing factors including topography, geology, soil, and land use. We used 2010 flood data for training and 2011 data for model validation. Frequency ratio (FR) and logistic regression (LR) models were applied to 2010 flood data to determine the relationships between the flooded area and its causal factors and to derive flood-susceptibility maps, which were substantiated using the area flooded in 2011 (not used for training). As a result of the accuracy validation, FR and LR model results were shown to have 79.61% and 79.05% accuracy, respectively. In terms of sustainability, floods affect water health as well as causing economic and social damage. These maps will provide useful information to decision makers for the implementation of flood-mitigation policies in priority areas in urban sustainable development and for flood prevention and management. In addition to this study, further analysis including data on economic and social activities, proximity to nature, and data on population and building density, will make it possible to improve sustainability.

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“…The WoE is the statistical-based Bayesian bivariate model that has been utilized for LSM process in recent literature (Lee et al, 2004;Dahal et al, 2008aDahal et al, , 2008bOh and Lee, 2011;Park, 2011;Althuwaynee et al, 2012;. The WoE is a useful model for evaluation of the relationship between landslide occurrence locations and related conditioning factors.…”

Section: Weights-of-evidence (Woe) Modelmentioning

confidence: 99%

Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS

et al. 2015

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This paper presents an evaluation of two bivariate statistical approaches, frequency ratio (FR) and weights-of-evidence (WoE) for landslide susceptibility mapping (LSM) at south-western part of Saudi Arabia, Jizan region. Landslide locations were identified and mapped from interpretation of high resolution satellite images (GeoEye 0.5 m and QuikeBird 0.6m), topographic maps (scale of 1:10,000), historical records, and extensive field surveys. A total of 106 landslide locations were mapped using Arc-GIS software and divided into two groups, 75% and 25% of landslide locations were used for training and validation of models, respectively. Eleven landslide conditioning factors such as elevation, slope, curvature, aspect, lithology, topographic wetness index (TWI), normalized difference vegetation index (NDVI), proximity to lineament, roads and rivers were considered in this evaluation. The effects of these factors on landslide occurrence were assessed using aforementioned bivariate statistical approaches. For validation, the models results were compared with landslide locations which were not used during the models building. Subsequently, the receiver operating characteristic (ROC) curves were established and area under the curves (AUC) was calculated for the landslide susceptibility maps using the success (training data) and prediction (validation data) rates. The results showed that the area under the curve for success rates are 0.861 (86.1%) and 0.839 (83.9%) and for prediction rates are 0.796 (79.6%) and 0.791 (79.1%), respectively for frequency ratio and weight-of-evidence models. The resulting landslide susceptibility maps showed five classes of susceptibility such as very high, high, moderate, low, and very low. The percentage of existing training and validating landslides data in high and very high zones of the susceptibility maps were calculated to be 90.02% and 76.03% for frequency ratio model and 88.33% and 79.3% for weight-of-evidence model, respectively. The results revealed that the frequency ratio and weights-of-evidence models produced reasonable accuracy. The resultant maps would be useful and can also help planners for general choosing favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection.

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Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea

Cited by 39 publications

References 42 publications

A new region-based preparatory factor for landslide susceptibility models: the total flux

A new region-based preparatory factor for landslide susceptibility models: the total flux

Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools

Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS

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