Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

Lee, Saro; Ryu, Ja‐Hyoung; Won, Joong-Sun; Park, Hyuck‐Jin

doi:10.1016/s0013-7952(03)00142-x

Cited by 516 publications

(237 citation statements)

References 15 publications

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“…Lee 2005;Goetz et al 2011Goetz et al , 2015Youssef et al 2016;Castro Camilo et al 2017) and Machine-Learning approaches (e.g. Lee et al 2004;Ermini et al 2005;Marjanovic et al 2011;Pham et al 2017) are also used in this type of analyses. Such approaches are very sensitive to the type and quality of the factors chosen for the susceptibility analysis, and the lack of suitable expert opinion can produce unreliable results (Soeters and Van Westen 1996).…”

Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

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In the last years, great efforts have been made to improve the assessment of the temporal and spatial occurrence of rainfall-induced shallow landslides. Therefore, in this paper we used a physically based stability model (TRIGRS) in order to reproduce the landslide event occurred in the Monterosso catchment (Cinque Terre, Eastern Liguria, Italy) on 25 October 2011. The input parameters of the numerical model have been evaluated taking into account the land-use setting and paying specific attention to the evaluation of the spatial variation of soil thickness on terraced areas. The resulting safety factor maps have been compared with the inventory map of the landslides triggered during the event. The simulation results, which have been obtained also considering four different spatial resolutions of the digital terrain model, emphasize the influence of land use in shallow landslide occurrence and indicate the importance of a realistic spatial variation of soil thickness to enhance the reliability of the model. Finally, different triggering scenarios have been defined using hourly rainfall values statistically derived from historical data. The results indicate the proneness of the area to shallow landsliding, given that rainfall events with a relatively low return period (e.g. 25 years) can trigger numerous slope failures.

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Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

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“…The urban environmental engineering and geological quality assessment have been adequately addressed by employing multivariate statistical analysis and GIS (Matula, 1981;Cross, 2002;Lee et al, 2004;Sarkar et al, 2007). Researchers have also studied the urban land suitability analysis by means of fuzzy classification methods and multi-criteria analysis (Hall, 1992;Davidson, 1994;Store and Kangas, 2001;Bagdanaviciute and Jurijus, 2013), as well as urban ecological suitability assessment for urban development and planning via ecology methods (Lathrop and Bognar, 1998;Svoray et al, 2005;.…”

Section: Introductionmentioning

confidence: 99%

Land suitability assessment for post-earthquake reconstruction: A case study of Lushan in Sichuan, China

Qing

Li²,

2015

J. Geogr. Sci.

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Land suitability assessment (LSA) is one of the key processes of land-use planning. Given its particularity of land suitability assessment for post-earthquake reconstruction, this paper takes into account geological conditions, risk of disasters, water and land resources conditions, and eco-environmental suitability and emphasizes safety factor in the assessment. Taking the April 20, 2013 Mw 6.6 Lushan earthquake as a case, this assessment establishes factors system, uses GIS spatial analysis techniques and data of geology, topography, resources, and eco-environment to evaluate the land suitability for reconstruction. The results show that: (1) the spatial characteristics of land suitability for reconstruction at grid scale and administrative scale manifest that most of the piedmont plains in the east are suitable for large-scale population aggregation, industrialization, and urbanization development; and (2) for the six hard-hit counties, Mingshan is the preferred region for large-scale post-earthquake reconstruction due to its high construction index and suitable land per capita, and some plots of land in the valleys could be selected for in-situ small-scale reconstruction in Lushan. The land suitability assessment for post-earthquake reconstruction would be significant to making sound reconstruction planning for achieving sustainable regional development in the Mw 6.6 Lushan earthquake stricken area. This study could be used as a reference for the regions with similar events.

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“…) Binary logistic regression (BLR) Atkinson and Massari (1998), Ayalew and Yamagishi (2005), Bai et al (2010), Can et al (2005), Carrara et al (2008), Chauan et al (2010), Conforti et al (2012), Dai and Lee (2002), Davis and Ohlmacher (2002), Erener and Düzgün (2010), Mathew et al (2009), Nandi and Shakoor (2009), Nefeslioglu et al (2008, Ohlmacher and Davis (2003), Van den Eckhaut et al (2006 Classification and regression trees (CART) Felicísimo et al (2012), Vorpahl et al (2012) Artificial neuronal networks (ANN) Aleotti and Chowdhury (1999), Ermini et al (2005), Lee et al (2004), Pradhan and Lee (2010) Original Paper exploited to compare the fitting of the model having only the constant term (all the β p are set to 0) with the fitting of the model that includes all the considered predictors with their estimated non-null coefficients so as to verify if the increase in likelihood is significant; in this case, at least one of the p coefficients is to be expected as different from zero (Hosmer and Lemeshow 2000). By exponentiating the β's, odds ratios (OR) for the independent variables are derived: these are measures of association between the independent variables and the outcome of the dependent, and directly express how much more likely (or unlikely) it is for the outcome to be positive (unstable cell) for unit changing of the considered independent variable.…”

Section: Statistical Techniquementioning

confidence: 99%

Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

et al. 2013

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Forward logistic regression has allowed us to derive an earth-flow susceptibility model for the Tumarrano river basin, which was defined by modeling the statistical relationships between an archive of 760 events and a set of 20 predictors. For each landslide in the inventory, a landslide identification point (LIP) was automatically produced as corresponding to the highest point along the boundary of the landslide polygons, and unstable conditions were assigned to cells at a distance up to 8m. An equal number of stable cells (out of landslides) was then randomly extracted and appended to the LIPs to prepare the dataset for logistic regression. A model building strategy was applied to enlarge the area included in training the model and to verify the sensitivity of the regressed models with respect to the locations of the selected stable cells. A suite of 16 models was prepared by randomly extracting different unoverlapping stable cell subsets that have been appended to the unstable ones. Models were finally submitted to forward logistic regression and validated. The results showed satisfying and stable error rates (0.236 on average, with a standard deviation of 0.007) and areas under the receiver operating characteristic (ROC) curve (AUCs) (0.839 for training and 0.817 for test datasets) as well as factor selections (ranks and coefficients). As regards the predictors, steepness and large-profile and local-plan topographic curvatures were systematically selected. Clayey outcropping lithology, midslope drainage, local and midslope ridges, and canyon landforms were also very frequently (from eight to 15 times) included in the models by the forward selection procedures. The model-building strategy allowed us to produce a performing earth-flow susceptibility model, whose model fitting, prediction skill, and robustness were estimated on the basis of validation procedures, demonstrating the independence of the regressed model on the specific selection of the stable cells.

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Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

Cited by 516 publications

References 15 publications

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Land suitability assessment for post-earthquake reconstruction: A case study of Lushan in Sichuan, China

Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

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