GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region

Akgün, Aykut; Bulut, Fikri

doi:10.1007/s00254-006-0435-6

Cited by 190 publications

(70 citation statements)

References 25 publications

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“…This method develops a LS map by combining various factor maps corresponding to the conditioning parameters [36]. Here, in order to quantify the impact of various classes for the standardization of each factor, we used the trapezoidal fuzzy number weighting (TFNW).…”

Section: Expert-based Fuzzy Weighting (Efw) Methodsmentioning

confidence: 99%

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GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method

Chalkias¹,

Ferentinou

Polykretis

2014

IJGI

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Abstract:The main aim of this paper is landslide susceptibility assessment using fuzzy expert-based modeling. Factors that influence landslide occurrence, such as elevation, slope, aspect, lithology, land cover, precipitation and seismicity were considered. Expert-based fuzzy weighting (EFW) approach was used to combine these factors for landslide susceptibility mapping (Peloponnese, Greece). This method produced a landslide susceptibility map of the investigated area. The landslides under investigation have more or less same characteristics: lateral based and downslope shallow movement of soils or rocks. The validation of the model reveals, that predicted susceptibility levels are found to be in good agreement with the past landslide occurrences. Hence, the obtained landslide susceptibility map could be acceptable, for landslide hazard prevention and mitigation at regional scale.

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Section: Expert-based Fuzzy Weighting (Efw) Methodsmentioning

confidence: 99%

“…Some qualitative approaches however incorporate the idea of ranking and weighting the parameters involved, and may turn to be semi-quantitative in nature [34][35][36][37]. The use of quantitative methods should not be seen as an easier option than the quantitative methods.…”

Section: Introductionmentioning

confidence: 99%

GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method

Chalkias¹,

Ferentinou

Polykretis

2014

IJGI

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“…4 shows that 8 contours (No. 7,16,17,21,23,25,28,29) represent the areas of low or very low risk of hazard of sliding. Although the risk of landside is lessened due to the spread of low-incline slopes, the seismicity reaches up to 8 points, while rocks are of high hazard of sliding.…”

Section: Figure 5 Landscape and Environmental Risks And Hazards Causmentioning

confidence: 99%

The Assessment of Landscape and Environmental Risks and Hazards Caused by Landslides in Mountain Areas

Alizade¹

2016

Appl Ecol Env Res

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Abstract. The landslide is a kind of natural disaster responsible for creation of risks and hazards in relation to population and its economic activity. Each year the occurrence of landslides causes killing of people and economic losses, as well as leads to destruction and deterioration of pastures, arable lands, forests and highways. The southeastern slope of Greater Caucasus is one of the most active landslide zones in Azerbaijan. As a result of the occurrence of landslide in the Mughanli area in 2011, 168 houses out of 279 ones became unusable while the village of the same name was moved to a new residential area. Moreover, the 3 km-long part of the Ismayilli-Mughanli-Shamakhi highway near the village became unusable repeatedly. In 2014, USD 52 million was spent in order to eliminate the consequences of landslides. Topographic maps of scale of 1:10000 and also aerospace images were used to assess the landscape-and environmental risks and hazards caused by landslides. Maps of various factors of the same scales, compiled in ArcGIS program were analysed through GIS technologies, which allowed compilation of a map of relevant landscape-and environmental risks and hazards. The carried out studies will enable to estimate natural disasters and reduce the related economic losses.

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“…In Turkey, mass movements are one of the most hazardous natural processes. Recently, some international papers have been published on the 558 M. C. Tunusluoglu et al: ANN application to produce debris source areas landslide mechanisms and landslide susceptibility mapping (Gokceoglu and Aksoy, 1996;Duman et al, 2006;Akgun and Bulut, 2007;Yalcin, 2007). However, these studies were applied in northern part of Turkey.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

Tunusluoglu

Gökçeoğlu

Sönmez

et al. 2007

Nat. Hazards Earth Syst. Sci.

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Abstract. Various statistical, mathematical and artificial intelligence techniques have been used in the areas of engineering geology, rock engineering and geomorphology for many years. However, among the techniques, artificial neural networks are relatively new approach used in engineering geology in particular. The attractiveness of ANN for the engineering geological problems comes from the information processing characteristics of the system, such as nonlinearity, high parallelism, robustness, fault and failure tolerance, learning, ability to handle imprecise and fuzzy information, and their capability to generalize. For this reason, the purposes of the present study are to perform an application of ANN to a engineering geology problem having a very large database and to introduce a new approach to accelerate convergence. For these purposes, an ANN architecture having 5 neurons in one hidden layer was constructed. During the training stages, total 40 000 training cycles were performed and the minimum RMSE values were obtained at approximately 10 000th cycle. At this cycle, the obtained minimum RMSE value is 0.22 for the second training set, while that of value is calculated as 0.064 again for the second test set. Using the trained ANN model at 10 000th cycle for the second random sampling, the debris source area susceptibility map was produced and adjusted. Finally, a potential debris source susceptibility map for the study area was produced. When considering the field observations and existing inventory map, the produced map has a high prediction capacity and it can be used when assessing debris flow hazard mitigation efforts.

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GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region

Cited by 190 publications

References 25 publications

GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method

GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method

The Assessment of Landscape and Environmental Risks and Hazards Caused by Landslides in Mountain Areas

An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

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