Assessing the factor of safety using an artificial neural network: case studies on landslides in Giresun, Turkey

Gelişli, Kenan; Kaya, Türkan; Babacan, Ali Erden

doi:10.1007/s12665-015-4027-1

Cited by 36 publications

(16 citation statements)

References 23 publications

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“…From the validation of the landslide susceptibility maps, the RBF kernel produced AUC values, indicating the accuracy of the landslide susceptibility maps, and these were 81.36% for the PyeongChang area, and 77.49% for the Inje area (Figure 7). There were some differences in accuracy between the study areas, because the previous studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][29][30][31][32][33] showed that the spatial distribution is subject to change, according to the area and event. However, the accuracy was usually high enough, displaying figures of above 80%.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, recent case studies have frequently applied soft computing technology to the assessment of landslide hazards. When creating soft computing models, artificial neural networks [2][3][4][5][6], neuro-fuzzy logic [2,[7][8][9], decision trees [10][11][12][13][14][15], and support vector machines (SVMs) [10,[15][16][17][18][19], have been applied in order to analyze landslide landslide susceptibility. Among the many soft computing models, SVMs were applied in the present study.…”

Section: Introductionmentioning

confidence: 99%

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A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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Abstract:In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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“…In recent years, grey system models, time series models, neural network models, extreme learning machines, sup2182 T. Wen et al: Landslide displacement prediction using the GA-LSSVM model for landslide displacement prediction (Wang, 2003;Pradhan et al, 2014;Gelisli et al, 2015;Goetz et al, 2015;Kavzoglu et al, 2015). Previously, landslide susceptibility maps were assessed using a back propagation (BP) artificial neural network and logistic regression analysis (Nefeslioglu et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

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“…According to China's National Bureau of Statistics, the proportion of total economic output in the Eastern Region of China remained stable at around 57% from 1995 to 2015, consistently higher than the sum of the total economic output of the Central and Western regions. Therefore, accelerating economic development in the Central and Western regions, narrowing the development gap between the Eastern Regions and other regions, and realizing a coordinated economic development strategy across these three data envelopment analysis (DEA) model [22], principal component analysis (PCA), analytic hierarchy process (AHP) [23][24][25], and the fuzzy comprehensive evaluation method [26] among others. These methods contain strict restrictions and requirements, including the number of indexes, sample capacity and data distribution.…”

Section: Introductionmentioning

confidence: 99%

Measuring and Spatio-Temporal Evolution for the Late-Development Advantage in China’s Provinces

Liu

Sun

et al. 2018

Sustainability

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The coordinated development of regional economies is a major economic goal of many countries around the world. To that end, China has actively carried out a series of strategies to expedite the development of its late-developing regions. This study explores the issues raised by this coordinated development from the perspective of late-development advantages, which refer to a region's late-development advantages compared with the early-developing regions in the country. The 15 indicators applied for evaluating the late-development advantages fall into five categories including capital, technology, industrial structure, institutions and human resources. Then, the model of entropy-weighted technique for order preference by similarity to an ideal solution (EW-TOPSIS) is applied to evaluate the late-development advantages of China's provinces. Following this, ArcGIS and GeoDa are used to analyze the spatio-temporal evolution pattern of the late-development advantages of China's provinces, and to compare the spatio-temporal effect of these advantages between the provinces. The results show that the overall late-development advantages of China's provinces had a downward trend from 2006 to 2015, with the Eastern Region falling by 8.07%, the Central Region falling by 14.37% and the Western Region falling by 8.05%, indicating that the development gap between China's Eastern and Western Regions is still large. The temporal effect analysis shows the temporal autocorrelation changes from positive status to negative status with the increase of lagging order, which means the trend of late-development advantage will reverse over time. The spatial effect analysis shows there were only significant Low-Low and Low-High aggregation in 2006 and 2010, but significant High-High and High-Low aggregations emerge in 2012 and 2015, implying that the development environment has effectively promoted the use of the provincial late-development advantage. The research results could provide theoretical basis for the policy making of the accelerating development of late-developing regions in China.

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Assessing the factor of safety using an artificial neural network: case studies on landslides in Giresun, Turkey

Cited by 36 publications

References 23 publications

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Measuring and Spatio-Temporal Evolution for the Late-Development Advantage in China’s Provinces

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