Predicting landslides for risk analysis — Spatial models tested by a cross-validation technique

Chung, Chang-Jo; Fabbri, Andrea G.

doi:10.1016/j.geomorph.2006.12.036

Cited by 125 publications

(83 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The area under the curve (AUROC) varies between 0.5 and 1.0, with higher values indicating a better fit of the model. The model is validated by comparing it to a landslide dataset that is not used for calibrating the model [30,31]. The subdivision of the landslide data into the training and validation parts is generally performed randomly or using non-overlapping temporal or spatial subsets of the landslide inventory [30].…”

Section: Validation Of Susceptibility Assessmentmentioning

confidence: 99%

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

et al. 2017

View full text Add to dashboard Cite

Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan.

show abstract

Section: Validation Of Susceptibility Assessmentmentioning

confidence: 99%

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Flooding = 0 Lucerne, clover, potato, wheat, vegetables, barley, maize -is a restrictive parameter -the lack of flooding areas reveals their favourability -flooding areas emphasize the retroactivity for all crops (Chung and Fabbri, 2008). To achieve validation, two surfaces were randomly selected and the results were compared with those areas considered suitable for respective crops.…”

Section: Database and Methodologymentioning

confidence: 99%

Identification of Land Suitability for Agricultural Use by Applying Morphometric and Risk Parameters Based on GIS Spatial Analysis

Bilașco

Roșca

Păcurar

et al. 2016

Not Bot Horti Agrobo

View full text Add to dashboard Cite

Agricultural land is one of the main resources for the development of rural communities and the peripheries of urban centres. An area of 936 km 2 , belonging to Intercommunity Association for Development Alba-Iulia, Transylvania region, Romania, was analysed in order to identify suitable land for agricultural use. This approach represents the stage preceding the identification of crops favourability for agricultural land, thus reducing the time and resources needed for the proper land evaluation mark. The extension of suitable surfaces for agricultural crops was realized using a GIS model based on spatial analysis, taking into account morphometric parameters (slope, altitude, slope orientation, the density of fragmentation) and the risk factors (probability of landslides, flooding, temperature and rainfall). The outcome of the case study was an agricultural land suitability map of the investigated area, which provides valuable information regarding areas suitable for crops. By applying this model, a better management of agricultural lands can be assured, representing an alternative to the classic method of evaluation marks. The proposed model was validated by comparing the results with the grades of crop suitability, method achieved through the land evaluation mark.Keywords: agricultural land, database, favourable crops, GIS model, maps cultivation, having as main sales market the polarizing urban centres.The GIS model shown in the present article was intended to facilitate the agricultural land evaluation mark; the main purpose of was to reduce the time provided for the identification of favourability for some agricultural crops by imposing restrictiveness among the territory involved in the research. Using GIS technology with the purpose of identifying these territories has provided remarkable results for land evaluation mark (Dumitru et al., 2010;Bhandari et al., 2013;Halder, 2013; Roşca et al., 2015).Achieving the spatial analysis model relies on the morphometric and risk parameters of the studied territory, parameters that have the greatest influence on land suitability and on the arrangement of different types of crops.

show abstract

“…The role of GIS in mass movement studies in respect of theory and methods has been discussed by Alexander (2008). Studies by Gorsevski et al (2003Gorsevski et al ( , 2005, Kanungo et al (2006), Chung and Fabbri (2008 and utilized the fuzzy set model to generate a landslide hazard prediction map. figure 1(b)).…”

Section: Geomatics Of Landslide Hazard Susceptibility Zonationmentioning

confidence: 99%

Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India

Srivastava

Lakhera

2010

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Himalaya, being an active orogen, is continuously undergoing tectonic activities that pose threats of land stability on the mountains. A large number of mass movement occurrences have been observed in a part of the Tons river valley area. Remote sensing and geographical information system (GIS) based techniques have been used to derive and analyse various parameters and attributes pertinent to the landslide hazards. The GIS based analysis in the present work, incorporating the fuzzy membership values from the existing landslides, has been used for spatial modelling of the parameter maps including geology, slope amount, slope aspect, weathering, erosion, drainage influence, tectonic influence and landuse/land cover for landslide hazard zonation. The model has been used to spatially classify the study area into zones of very high, high, moderate, low and very low landslide hazard zones. Eighty-five percent of the active landslides have been observed to occur in very high and high hazard zones.

show abstract

Predicting landslides for risk analysis — Spatial models tested by a cross-validation technique

Cited by 125 publications

References 11 publications

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

Identification of Land Suitability for Agricultural Use by Applying Morphometric and Risk Parameters Based on GIS Spatial Analysis

Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India

Contact Info

Product

Resources

About