A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping

Vahidnia, Mohammad H.; Alesheikh, Ali Asghar; Alimohammadi, Abbas; Hosseinali, Farhad

doi:10.1016/j.cageo.2010.04.004

Cited by 192 publications

(75 citation statements)

References 32 publications

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“…Numerous studies can be found in the literature regarding the assessment of landslide susceptibility. For example, Oh et al (2011) applied an adaptive neurofuzzy system (ANFIS) to map the landslide susceptibility (Oh et al, 2011;Gemitzi et al, 2010;Vahidnia et al, 2010). Neuro-fuzzy networks are systems which originate from the union of neural networks and fuzzy inference.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Sdao

Lioi

Pascale

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Abstract. The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the determination of the landslide susceptibility map.

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Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Sdao

Lioi

Pascale

et al. 2013

Nat. Hazards Earth Syst. Sci.

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“…As the methodological proposal of this study includes techniques and data obtained from the public domain, the validation of the results of the fuzzy gamma technique was not done through landslide-scar maps, that is, a validation technique commonly used in related studies Dymond et al, 2006;Ercanoglu and Gokceoglu, 2004;Vahidnia et al, 2010). Alternatively, the CPRM risk sectors were used as units of validation, since in Brazil, there are no free data of landslide-scars mapped or monitored.…”

Section: Validation Of Resultsmentioning

confidence: 99%

Shallow landslide prediction and analysis with risk assessment using a spatial model in a coastal region in the state of São Paulo, Brazil

Camarinha

Canavesi

Alvalá

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. This study presents a methodology for susceptibility mapping of shallow landslides just from data and software from the public domain. The study was conducted in a mountainous region located on the southeastern Brazilian coast, in the state of São Paulo. The proposal is that the methodology can be replicated in a practical and reliable way in several other municipalities that do not have such mappings and that often suffer from landslide-related disasters. The susceptibility mapping was generated based on the following maps: geological, soils, slope, horizontal and vertical curvatures, and land use. The thematic classes of these maps were weighted according to technical and scientific criteria related to the triggering of landslides, and were crossed by the fuzzy gamma technique. The mapping was compared with the risk sector survey made by the Brazilian Geological Survey (CPRM), which is the official database used by municipalities and civil defense in risk management. The results showed positive correlations, so that the critical risk sectors had higher proportions for the more susceptible classes. To compare the approach with other studies using landslide-scar maps, correlated indices were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas.

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“…Hybrid methods, which are established by combining statistical approaches and artificial intelligence, have also been adopted for assessing geological hazards; these include artificial neural network (ANN)-Bayes analysis [14], ANN-fuzzy logic [26], and neuro-fuzzy inference systems [27,28]. However the ANN-based approaches cannot provide objective and steady assessment results because their outcomes are operator dependent [13,15].…”

Section: Introductionmentioning

confidence: 99%

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

Chen

Shang

2016

Sustainability

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Abstract:Assessing the failure potential of slopes is of great significance for land use and management. The objective of this paper is to develop a novel model for evaluating the failure probability of slopes based on a relevance vector machine (RVM), with a special attention to the characteristics of failed slopes along the lower reaches of the Jinsha River, close to the Wudongde dam site. Seven parameters that influence the occurrence of landslides were selected as environmental factors; namely lithology, slope angle, slope height, slope aspect, slope structure, distance from faults, and land use. A total of 55 landslides mapped in the study area were used to train and test the RVM model. The results suggest that the accuracy of the model in predicting the failure probability of slopes, using both training and testing data sets, is very high and deemed satisfactory. To validate the model performance, it was applied to 28 landslide cases identified in the upper reaches of the Jinsha River, where environmental and geological conditions are similar to those of the study area. An accuracy of approximately 92.9% was obtained, which demonstrates that the RVM model has a good generalization performance.

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A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping

Cited by 192 publications

References 32 publications

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Shallow landslide prediction and analysis with risk assessment using a spatial model in a coastal region in the state of São Paulo, Brazil

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

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