Effects of strong ground motion on the susceptibility of gully type debris flows

Liu, Chia Nan; Dong, Jia Jyun; Peng, Yih‐Ferng; Huang, Hsiao Fung

doi:10.1016/j.enggeo.2008.10.012

Cited by 46 publications

(17 citation statements)

References 24 publications

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“…The vegetation cover ratio is described as the ratio of the vegetation area to the watershed area [10]. The dominant species in the study area are grasses on the soils.…”

Section: Vegetation Cover Ratiomentioning

confidence: 99%

“…In fact, to establish a practical empirical model, large datasets are necessary. Artificial intelligence models, such as genetic algorithm [6], artificial neural network [7][8][9][10] and support vector machine [11] have been applied for debris flow prediction. Most of these models have been created using regional debris flow inventories derived from remotely sensed data.…”

Section: Study Areamentioning

confidence: 99%

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Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Wang

Chen

et al. 2017

Water

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Debris flows in the Wudongde dam area, China could pose a huge threat to the running of the power station. Therefore, it is of great significance to carry out a susceptibility analysis for this area. This paper presents an application of the rock engineering system and fuzzy C-means algorithm (RES_FCM) for debris flow susceptibility assessment. The watershed of the Jinsha River close to the Wudongde dam site in southwest China was taken as the study area, where a total of 22 channelized debris flow gullies were mapped by field investigations. Eight environmental parameters were selected for debris flow susceptibility assessment, namely, lithology, watershed area, slope angle, stream density, length of the main stream, curvature of the main stream, distance from fault and vegetation cover ratio. The interactions among these parameters and their weightings were determined using the RES method. A debris flow susceptibility map was produced by dividing the gullies into three categories of debris flow susceptibility based on the susceptibility index (SI) using the FCM algorithm. The results show that the susceptibility levels for nine of the debris flow gullies are high, nine are moderate and four are low, respectively. The RES based K-means algorithm (RES_KM) was used for comparison. The results suggest that the RES_FCM method and the RES_KM method provide very close evaluation results for most of the debris flow gullies, which also agree well with field investigations. The prediction accuracy of the new method is 90.9%, larger than that obtained by the RES_KM method (86.4%). Therefore, the RES_FCM method performs better than the RES_KM method for assessing the susceptibility of debris flows.

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“…The vegetation cover ratio is described as the ratio of the vegetation area to the watershed area [10]. The dominant species in the study area are grasses on the soils.…”

Section: Vegetation Cover Ratiomentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Wang

Chen

et al. 2017

Water

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“…Conventionally, the fault length through the watershed or distance between fault and gully is selected as one of the factors dominating the occurrence of debris flow (Lin et al 2002;Liu et al 2009) or landslides (Lan et al 2004;Lee et al 2008;Kincal et al 2009). This case demonstrates the rational for adopting the geological structure factor in susceptibility analysis of debris flows or landslides.…”

Section: Discussionmentioning

confidence: 99%

Typical landslides and related mechanisms in Ali Mountain highway induced by typhoon Morakot: perspectives from engineering geology

et al. 2011

Self Cite

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Typhoon Morakot caused serious damage to southern Taiwan. Almost all major mountain highways located in the affected region suffered severe damage from landslides, rock falls, and debris flows. Among these highways, Ali Mountain Highway is one of the most severely affected. Ali Mountain Highway, i.e., T18 route, was constructed in the early 1980s. For the past 30years of operation, it has suffered substantial damage from different natural hazards. The continued damage-rehabilitation cycle is implemented to maintain the serviceability of this highway. Ali Mountain Highway was seriously damaged by the strike of typhoon Morakot on August 8, 2009. Traffic through this highway was interrupted for over 3months until temporary rehabilitation was completed. This paper provides a summary of the damage suffered along the Ali Mountain Highway and presents suggestions for mitigation strategies to minimize the likelihood of similar levels of damage during other extreme typhoons such as the typhoon Morakot. The manuscript summarizes extensive field surveys and aerial photo analysis carried out by the authors to investigate the mechanisms of typical slope failures along the Ali Mountain Highway. The engineering geology perspectives are emphasized in the analyses. Geological and hydrogeological features for the sites in this study were found to be the most influential factors dominating the failures. The fault-related weak zones and impermeable layers dominated the global stability of the slopes. In summary, geotechnical engineers should consider carefully the characteristics of geological structures for mitigation and rehabilitation of landslides. Extreme scenarios, such as typhoon Morakot, should also be considered.

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“…Three fundamental categories of variables, which describe three aspects, namely topography, geology, and hydrogeology, are used to determine debris flow trigger (Liu and Tang 1995;Liu et al 2009). The selection of influencing factors is necessary to determine debris flow hazard degree.…”

Section: Selecting the Influencing Factorsmentioning

confidence: 99%

A novel hybrid KPCA and SVM with PSO model for identifying debris flow hazard degree: a case study in Southwest China

Qian

Chen

Xiang³

et al. 2016

Environ Earth Sci

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A novel support vector machine (SVM) model that combines kernel principal component analysis (KPCA) with particle swarm optimization (PSO) is proposed for identifying debris flow hazard degree. The proposed model consists of three stages. First, KPCA is used to extract nonlinear feature information and solve the linear correlation of input data. Second, PSO is implemented to optimize the selection of the penalty factor and the kernel function parameter of the SVM classifier in the solution space. This optimization can prevent blindness in parameter selection and improve the identification accuracy of the model. Finally, the extracted new features and the optimal parameters for the SVM classifier are employed to predict the test data. Experimental results show that the proposed model can more effectively select discriminating input features and achieve higher identification accuracy than other hybrid models. Therefore, the proposed model exhibits high potential to become a useful tool for identifying debris flow hazard degree in limited samples.

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Effects of strong ground motion on the susceptibility of gully type debris flows

Cited by 46 publications

References 24 publications

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

Typical landslides and related mechanisms in Ali Mountain highway induced by typhoon Morakot: perspectives from engineering geology

A novel hybrid KPCA and SVM with PSO model for identifying debris flow hazard degree: a case study in Southwest China

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