A Geographical Information System (GIS) Based Probabilistic Certainty Factor Approach in Assessing Landslide Susceptibility: The Case Study of Kimi, Euboea, Greece

Ilia, Ioanna; Koumantakis, I.; Ρόζος, Δ.; Koukis, G.; Tsangaratos, Paraskevas

doi:10.1007/978-3-319-09057-3_210

Cited by 16 publications

(9 citation statements)

References 27 publications

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“…CF value nearby 0 indicates that the conditional probability is very close to the prior probability, and hence it is difficult to ascertain any certainty of landslide occurrence (Lee and Talib, 2005;Pourghasemi et al, 2012e). The pairwise data layers of all landslide causative factors were combined based on "Z" values obtained from Equation ( 6) (Dou et al 2014;Ilia et al 2015).…”

Section: Cf =mentioning

confidence: 99%

Evaluating the impact of road construction on landslide susceptibility- A case study of Mandi district, Himachal Pradesh, India.

Sharma¹,

Prakash²

2021

Preprint

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Landslide susceptibility mapping has proved to be crucial tool for effective disaster management and planning strategies in mountainous regions. The present study is perused to investigate the changes in the landslide susceptibility of the Mandi district of Himachal Pradesh due to road construction. For this purpose, an inventory of 1723 landslides was generated from various sources. Out of these, 1199 (70%) landslides were taken in the training dataset to be used for modelling and prediction purposes, while 524 (30%) landslides were taken in the testing dataset to be used for validation purposes. Eleven landslide causative factors were selected from numerous hydrological, geological and topographical factors and were analyzed for landslide susceptibility mapping using three bivariate statistical models, namely; Frequency Ratio (FR), Certainty Factor (CF) and Shanon Entropy (SE). Two sets of LSM maps i.e. landslide susceptibility map natural (LSMN) and landslide susceptibility map road (LSMR), were generated using the above mentioned bivariate models and were divided into five landslide susceptibility classes namely; very low, low, medium, high and very high. These maps were analyzed for accuracy of prediction and validation using receiver operating characteristic (ROC) curves and area under curve (AUC) technique which indicated that all three bivariate statistical models performed satisfactorily with the SE model had the highest prediction and validation accuracy of 83-86%. Further analysis LSM maps confirmed that the percentage area in high and very high classes of land-slide susceptibility increased by 2.67-4.17% due to road construction activities in the study area.

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Section: Cf =mentioning

confidence: 99%

Evaluating the impact of road construction on landslide susceptibility- A case study of Mandi district, Himachal Pradesh, India.

Sharma¹,

Prakash²

2021

Preprint

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“…Once obtained, the CF values inside the flood conditioning factors are pair-wise combined using the CF combination rule. The combination of two different CF values, X and Y from two different layers of information is the Z value of CF, which can be determined as follows (Ilia et al 2015…”

Section: Certainty Factor (Cf)mentioning

confidence: 99%

Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management

Costache

2019

Water Resour Manage

130

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In Romania, as in the rest of the world, the flood frequency has increased considerably. Prahova river basin is among the most exposed catchments of the country to flood risk. It also represents the area of the present study for which the identification of surfaces with high susceptibility to flood phenomena was attempted by applying 2 hybrid models (adaptive neuro-fuzzy inference system and fuzzy support vector machine hybrid) and 2 bivariate statistical models (certainty factor and statistical index). The computation of Flood Potential Index (FPI) was possible by considering a number of 10 flood conditioning factors together with a number of 158 flood pixels and 158 non-flood pixels. Generally, the high and very high flood potential appears on around 25% of the upper and middle basin of Prahova river. The validation of the results was made through the ROC Curve model. One of the novelties of this research is related to the application of Fuzzy Support Vector Machine ensemble for the first time in a study concerning the evaluation of the susceptibility to a certain natural hazard.

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“…e second model is to establish the function relationship or expression between the landslide and factors by selecting an appropriate mathematical means, so as to conduct landslide susceptibility mapping, for example, frequency ratio [17][18][19], weights-of-evidence [20][21][22][23], certainty factors [24][25][26], and logistic regression [27][28][29]. e occurrence of landslides is complicated nonlinear and affected by conditioning factors, including geomorphological, geological, hydrological, surface cover index, geophysical, and meteorological factors [30,31].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

Zhao

2021

Mathematical Problems in Engineering

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The main purpose of this study aims to apply and compare the rationality of landslide susceptibility maps using support vector machine (SVM) and particle swarm optimization coupled with support vector machine (PSO-SVM) models in Lueyang County, China, enhance the connection with the natural terrain, and analyze the application of grid units and slope units. A total of 186 landslide locations were identified by earlier reports and field surveys. The landslide inventory was randomly divided into two parts: 70% for training dataset and 30% for validation dataset. Based on the multisource data and geological environment, 16 landslide conditioning factors were selected, including control factors and triggering factors (i.e., altitude, slope angle, slope aspect, plan curvature, profile curvature, SPI, TPI, TRI, lithology, distance to faults, TWI, distance to rivers, NDVI, distance to roads, land use, and rainfall). The susceptibility between each conditioning factor and landslide was deduced using a certainty factor model. Subsequently, combined with grid units and slope units, the landslide susceptibility models were carried out by using SVM and PSO-SVM methods. The precision capability of the landslide susceptibility mapping produced by different models and units was verified through a receiver operating characteristic (ROC) curve. The results showed that the PSO-SVM model based on slope units had the best performance in landslide susceptibility mapping, and the area under the curve (AUC) values of training and validation datasets are 0.945 and 0.9245, respectively. Hence, the machine learning algorithm coupled with slope units can be considered a reliable and effective technique in landslide susceptibility mapping.

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A Geographical Information System (GIS) Based Probabilistic Certainty Factor Approach in Assessing Landslide Susceptibility: The Case Study of Kimi, Euboea, Greece

Cited by 16 publications

References 27 publications

Evaluating the impact of road construction on landslide susceptibility- A case study of Mandi district, Himachal Pradesh, India.

Evaluating the impact of road construction on landslide susceptibility- A case study of Mandi district, Himachal Pradesh, India.

Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management

A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

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