Reliability of landslide isopleth maps

Bulut, Fikri; Boynukalin, S.; Tarhan, F.; Ataoglu, E.

doi:10.1007/s100640050002

Cited by 22 publications

(8 citation statements)

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“…Inventory and statistical approaches are suited for use at regional to national level, whereas application of deterministic approaches is restricted to studies with focus on modeling the stability of single slopes (Table 4.1). Most common approaches of inventory analysis are landslide location, isopleth or density maps that are able to inform about the spatial distribution and frequency of past landslides over broad areas (e.g., Wright et al 1974;DeGraff and Canuti 1988;Bulut et al 2000;. Such maps are the simplest types of hazard assessments, with density maps, for instance, assuming landslides to be a spatially continuous variable, thus showing the best results in case of homogenous (Guzzetti et al 2000).…”

Section: Introductionmentioning

confidence: 98%

Landslide Databases as Tools for Integrated Assessment of Landslide Risk

Klose

2015

Springer Theses

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Section: Introductionmentioning

confidence: 98%

Landslide Databases as Tools for Integrated Assessment of Landslide Risk

Klose

2015

Springer Theses

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“…Inventory-based methods are a prerequisite to all other methods, as they provide the most important input and are used to validate the resulting maps. Corominas et al (2014) recommend the following methods for obtaining landslide inventory: (1) landslide distribution maps based on remote sensing image interpretation, with generation of event-based inventories or multiple occurrence of regional landslide events (Wieczorek 1984;Crozier 2005;Cheng et al 2013;Scaioni et al 2014); (2) landslide activity maps based on multitemporal image interpretation (Keefer 2002;Reid and Page 2003;Lazzari et al 2018); (3) generation of inventories based on historical records (Guzzetti et al 2000;Jaiswal and van Westen 2009); (4) landslide inventory based on radar interferometry or LIDAR (Squarzoni et al 2003;Colesanti and Wasowski 2006;Tseng et al 2015); (5) representation of landslide inventory as spatial density information (Bulut et al 2000;Coe et al 2000;Valadão et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil

et al. 2019

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Empirical models based on machine learning methods have been used for landslide susceptibility mapping. The most accurate model is usually chosen to generate the final map. This paper demonstrates the importance of analyzing the spatial pattern of susceptibility maps, since models with similar performance can produce different output values. The relevance of terrain attributes and the sensitivity of models to input variables are also discussed. The applications of random forest (RF) and artificial neural network (ANN) models to the identification of landslide susceptible areas in the Fão River Basin, Southern Brazil, were evaluated and compared. The following have been included in the methodology: (1) the extraction of predictive attributes (e.g., slope, aspect, curvatures, valley depth) from a digital elevation model; (2) the organization of a landslide scar inventory; (3) the calibration and validation procedures of the models; (4) the analysis of model performance according to accuracy (area under the receiver operating characteristic curve) and parsimony (Akaike Information Criterion); (5) the reclassification of maps into susceptibility categories. All model configurations resulted in an accuracy above 0.9, demonstrating the ability of both techniques in landslide susceptibility mapping. The RF model stood out in this respect, recording the highest accuracy index among all tested configurations (0.949). The ANN model was more parsimonious, obtaining an accuracy of 0.925 with a much smaller number of internal connections. Thus, even with both having high and equivalent accuracy indexes, the models can establish different relationships between the input and the output susceptibility indexes, resulting in various possible landslide occurrence scenarios. These differences, together with the difficulty in defining which model presents more coherent results, reinforce the possibility of extracting spatial statistics, considering multiple configurations of models that combine accuracy and parsimony, in landslide susceptibility mapping.

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“…Interpreting the likely hood of future landslides occurrences requires an understanding of condition and process controlling past landslides in the area of interest (OAS/DRDE 2007). Landslide inventory analysis can be done by various methods such as i) landslide distribution maps based on image interpretation (event based inventories) ii) landslide activity maps based on multi temporal image interpretation iii) generating inventories based on historical records iv) inventory based on radar interferometry, v) representation of landslide inventory as density information, landslide isopleth maps (Wieczorek 1984;Crozier 2005;Keefer 2002;Reid and Page 2003;Guzetti et al 2000Guzetti et al & 2005Jaiswal and van Westen 2009;Squarzoni et al 2003;Colesanti and Wasowski 2006;Coe et al 2000;Bulut et al 2000;Valadao et al 2002). Even though many researchers carried out research on landslide in Nilgiri district there is no fully or fairly documented landslide inventory for the district.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of landslide hazard and its impacts on hilly environment of the Nilgiris District - a geospatial approach

Thennavan

Ganapathy

2020

Geoenviron Disasters

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Landslide Hazard Zonation (LSH) maps play a key role in landuse planning particularly in landslide prone areas. LSH mapping is globally accepted one for analyzing the area for landslide susceptibility. Different approaches were followed by many researchers in India to prepare landslide hazard zonation mapping depending upon their need and requirement. The Nilgiris district in Western Ghats of India is one of the severe to high landslide hazard prone areas of India. Many agencies have carried out research on LSH mapping for the Nilgiris district with different scales. A systematic study of inventory and zonation was 122carried out in 1980's by government agencies. However there is no proper updation or documentation on landslides after 1980's in the district The purpose of this paper is to review the existing landslide-related studies in the district of The Nilgiris and review the district's existing landslide hazard map with updated information. Landslide hazard maps in The Nilgiris were compiled in the GIS platform from various authenticated sources. Data on landslides from 1824 to 2014 were collected and a spatial database on landslides was created. A detailed inventory was analyzed and used for revision of the district's landslide hazard impact on the 2009 landslides.. Based on the landslide inventory and densely populated areas and repeated landslides at the same locations, the most landslide hazard areas were identified.

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Reliability of landslide isopleth maps

Cited by 22 publications

References 0 publications

Landslide Databases as Tools for Integrated Assessment of Landslide Risk

Landslide Databases as Tools for Integrated Assessment of Landslide Risk

Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil

Evaluation of landslide hazard and its impacts on hilly environment of the Nilgiris District - a geospatial approach

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