A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods

Abstract. Our study is aimed at estimating the added value provided by Numerical Weather Prediction (NWP) data for the modelling and prediction of rainfall-induced shallow landslides. We implemented a quantitative indirect statistical modelling of such phenomena by using, as input predictors, both geomorphological, geological, climatological information and numerical data obtained by running a limitedarea weather model. Two standard statistical techniques are used to combine the predictor variables: a generalized linear model and Breiman's random forests. We tested these models for two rainfall events that occurred in 2011 and 2013 in Tuscany region (central Italy). Modelling results are compared with field data and the forecasting skill is evaluated by mean of sensitivity-specificity receiver operating characteristic (ROC) analysis. In the 2011 rainfall event, the random forests technique performs slightly better than generalized linear model with area under the ROC curve (AUC) values around 0.91 vs. 0.84. In the 2013 rainfall event, both models provide AUC values around 0.7.Using the variable importance output provided by the random forests algorithm, we assess the added value carried by numerical weather forecast. The main results are as follows: (i) for the rainfall event that occurred in 2011 most of the NWP data, and in particular hourly rainfall intensities, are classified as "important" and (ii) for the rainfall event that occurred in 2013 only NWP soil moisture data in the first centimetres below ground is found to be relevant for landslide assessment. In the discussions we argue how these results are connected to the type of precipitation observed in the two events.

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“…Recently it has been used by several authors as a predictor in LSM (Floris et al, 2011;Catani et al, 2013;Demir et al, 2013;Devkota et al, 2013).…”

Section: Hydrology-related Predictorsmentioning

confidence: 99%

Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results

Capecchi

Perna

Crisci

2015

Nat. Hazards Earth Syst. Sci.

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“…More recently, some researchers have created landslide susceptibility maps using statistical models, and some of them combine those models with other approaches such as frequency ratio (FR) and logistic regression (LR) methods (e.g., by Lee and Pradhan 2007, Oh et al 2008, and Solaimani et al 2013. FR was combined with analytical heuristic approach (AHP) by Demir et al (2013) and Reis et al (2012), and combination using FR, AHP, LR and artificial neural network (ANN) model was proposed by Park et al (2013). Integrated techniques such as FR, weight of evidence (WoE) and deterministic methods have been applied by Cervi et al (2010) and Yilmaz and Keskin (2009).…”

Section: Introductionmentioning

confidence: 99%

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

Rasyid

Bhandary

Yatabe

2016

Geoenviron Disasters

175

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The purposes of this study is to create a landslide susceptibility map (LSM) for Lompobattang Mountain area in Indonesia. The foot of the Lompobattang Mountain area suffered flash flood and landslides in 2006, which led to significant adverse impact on the nearby settlements. There were 158 identified landslides covering a total area of 3.44 km 2 . Landslide inventory data were collected using google earth image interpretations. The landslide inventories were prepared out of the past landslide events, and future landslide occurrence was predicted by correlating landslide causal factors. In this study landslide inventories were divided into landslide data for training and landslide data for validation. The LSM was prepared by Frequency Ratio (FR) and Logistic Regression (LR) statistical methods. Lithology, distance from the road, distance from the river, distance from the fault, land use, curvature, aspect, and slope degree were used as conditioning parameters. Area under the curve (AUC) of the Receiver Operating Characteristic (ROC) was used to check the performance of the models. In the analysis, the FR model results in 85.8 % accuracy in the AUC success rate while the LR model was found to have 86.9 % accuracy. However, the accuracy of both these models in AUC predictive rate is the same at around 85.1 %. The LR model is 6.34 % higher than the FR model in comparison to its accuracy for ratio of landslide validation. The landslide susceptibility map consist of the predicted landslide area, hence it can be used to reduce the potential hazard associated with the landslides in this study area.

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“…The RFM is an easily applied probability model, and it is widely used especially in landslide susceptibility mapping literature [19,20,21,22,23,24]. The main assumption of these studies is "In general, it is necessary to assume that land slide occurrence is determined by land-slide-related factors, and future landslides will occur under the same conditions as past landslides" [25].…”

Section: Relative Frequency Methods (Rfm)mentioning

confidence: 99%

Identification of Accident-Prone Road Sections by Using Relative Frequency Method

Yakar

2015

PROMET

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A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods

Cited by 174 publications

References 53 publications

Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results

Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

Identification of Accident-Prone Road Sections by Using Relative Frequency Method

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