Assessment of debris flow hazards using a Bayesian Network

Liang, Wanjie; Zhuang, Dafang; Jiang, Dong; Pan, Jianjun; Ren, Haitao

doi:10.1016/j.geomorph.2012.05.008

Cited by 85 publications

(48 citation statements)

References 36 publications

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“…Recently, data mining techniques have been developed and are extremely popular [19,20] when dealing with a variety of nonlinear issues. Techniques applied in landslide susceptibility modeling include: artificial neural network, decision tree, boosted tree, neuro fuzzy, Bayesian network, support vector machine, and random forest [21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Lee

2017

Applied Sciences

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Abstract:The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN) and boosted tree (BT), for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.

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Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Lee

2017

Applied Sciences

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“…Thus, in that case we consider acceptable to add data on the magnitude of the studied characteristics, relevant increasing action of external agents, provoking catastrophic processes, or showing the morphosystem inner characteristics specificity (geomorphologic circumstances), also leading to increasing exogenous processes intensification. However, because of the different dimension and magnitude of the studied characteristics, the straight addition is not correct; thus, we, according to [Simonov, 1997;Liang at al., 2012], conducted "normalization" of the values by formula X' = (X -X min ):(X max -X min ), where Х is the real value of the characteristic, X max -X min is the range of its possible values, and X' -the normalized characteristic's value within the interval from 0 to 1. Also, for calculation convenience [Liang at al., 2012] we converted the fraction values into the whole values, and considered them as the relative tension points.…”

Section: Methodsmentioning

confidence: 99%

Tension of Geomorphologic Conditions in the Marginal Mountain Belts of the Pacific Rim

Лебедева

Mikhalev

Шварев

et al. 2016

GES

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“…However, when the study area is large, application of these approaches may be difficult. Statistical analyses such as discriminant analysis [4,7], logistic regression [8,9], and Bayes learning [10,11], are deemed to be more suitable for geological hazard assessment in large and complex areas [12,13]. With the development of science and technology, soft computing techniques, such as data mining and artificial intelligence, have also been widely used in geological hazard assessment.…”

Section: Introductionmentioning

confidence: 99%

“…The Bayes learning algorithm is considered to be an effective tool for knowledge representation and reasoning under the influence of uncertainty [10]. Based on this algorithm, a recently developed machine learning technique, relevance vector machine (RVM), was originally introduced by Tipping [29].…”

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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Assessment of debris flow hazards using a Bayesian Network

Cited by 85 publications

References 36 publications

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Tension of Geomorphologic Conditions in the Marginal Mountain Belts of the Pacific Rim

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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