Implication of subsurface flow on rainfall-induced landslide: a case study

Li, Wei Chao; Dai, Fuchu; Wei, Ying; Wang, Ming Long; Hong, Min-Sun; Lee, Min Lee

doi:10.1007/s10346-015-0619-9

Cited by 53 publications

(11 citation statements)

References 38 publications

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“…In the Qinghai region, large loess areas existed, where there are mountainous topographical features and have a high incidence of severe landslides. e occurrences of landslides are extremely complicated and affected by many factors such as geologic structure, lithological association, topography, rainfall, earthquake, and human activity [8][9][10][11][12]. Based on these factors, various assessing methods have been proposed to analyze the landslide susceptibility, which can be divided into two categories: qualitative analysis and quantitative analysis [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

Wang

Chen

2021

Advances in Civil Engineering

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Landslides are one of the nature hazards causing a lot of casualties and property losses in the world. Over the last decades, many researchers have made contributions in landslide susceptibility maps using qualitative and quantitative methods. Parameters of DEM, geology, etc. are selected to analyze the mechanism of landslides. The quality of data is essential in the landslide studies, and more credible results can be obtained if the data is adequate and accurate from the wide range of parameters. The aim of this study is to evaluate the landslide susceptibility of Huangyuan County of Qinghai. Through field investigations, 100 landslide disaster locations in the study area were selected, and 11 influencing factors including elevation, slope, aspect, plane curvature, profile curvature, road distance, river distance, fault distance, stratum rock property, vegetation coverage index, and terrain humidity index were selected as the influencing factors of landslide disaster based on GIS. In this paper, the information method (IM) model, frequency ratio (FR) model, and artificial neural network (ANN) model are used to evaluate the susceptibility of geological hazards, and the receiver operating characteristic (ROC) curve of disaster points at different levels is used to test the evaluation accuracy of three models. The results show that factors that have great influence on landslides are associated with witness, and the terrain humidity index has the highest weight in the occurrences of landslide. The values of AUC indicate that the ANN model is the best evaluation model suitable for the study area and can be extremely useful for landslide hazard mitigation strategies. Based on the calculation of ANN model, three valley areas are determined with high landslide susceptibility, and necessary reinforcement measures should be taken.

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Section: Introductionmentioning

confidence: 99%

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

Wang

Chen

2021

Advances in Civil Engineering

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“…To date, there have been many studies on the effects of rainfall on landslides. Research methods include in situ experiments, laboratory experiments, and numerical simulations [17,18]. The artificial rainfall test is an effective method when used to study rainfall-induced landslides [19].…”

Section: Introductionmentioning

confidence: 99%

The Effects of Rainfall, Soil Type and Slope on the Processes and Mechanisms of Rainfall-Induced Shallow Landslides

2021

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Landslides are a serious geohazard worldwide, causing many casualties and considerable economic losses every year. Rainfall-induced shallow landslides commonly occur in mountainous regions. Many factors affect an area’s susceptibility, such as rainfall, the soil, and the slope. In this paper, the effects of rainfall intensity, rainfall pattern, slope gradient, and soil type on landslide susceptibility are studied. Variables including soil volumetric water content, matrix suction, pore water pressure, and the total stress throughout the rainfall were measured. The results show that, under the experimental conditions of this paper, no landslides occurred on a 5° slope. On a 15° slope, when the rainfall intensity was equal to or less than 80 mm/h with a 1 h duration, landslides also did not happen. With a rainfall intensity of 120 mm/h, the rainfall pattern in which the intensity gradually diminishes could not induce landslides. Compared with fine soils, coarser soils with gravels were found to be prone to landslides. As the volumetric water content rose, the matrix suction declined from the time that the level of infiltration reached the position of the matrix. The pore water pressure and the total stress both changed drastically either immediately before or after the landslide. In addition, the sediment yield depended on the above factors. Steeper slopes, stronger rainfall, and coarser soils were all found to increase the amount of sediment yield.

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“…To date, there have been many studies on the effects of rainfall on landslides. The research methods include in situ experiments, laboratory experiments and numerical simulations [12,13]. The artificial rainfall test is an effective method to study rainfall-induced landslides [14].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Rainfall, Soil Type and Slope on the Processes and Mechanism of Rainfall-Induced Landslide

Liu¹,

Deng²,

Wang³

2021

Preprint

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Landslides are serious geological hazards that become a disaster worldwide, causing a large number of casualties and economic losses every year. There are many factors affecting landslide susceptibility, such as rainfall, soil and slope. Each of them has an important role in the process of slope losing stability. In this paper, the effects of rainfall intensity, rainfall pattern, slope gradient and soil type on landslide susceptibility are studied. In the process of rainfall-induced landslide, the relevant physical quantities of soil changes continuously. Their values and processes are closely related to the time of landslide occurrence. Hence, the variation of soil volumetric water content, matrix suction, pore water pressure and total stress throughout the rainfall are measured. As the results, soil type, slope gradient and rainfall intensity have a large influence on landslide susceptibility. The occurrence of landslides has a prerequisite that the slope is greater than or equal to 15°. The rainfall intensity needs to be not less than 80 mm/h. The difference of rainfall pattern also affects the landslide susceptibility. The rainfall pattern with rainfall intensity peak at the later stage is more likely to induce landslide. Coarser soils with gravels are prone to landslides when other conditions are the same. Steeper slopes, stronger rainfall, and coarser soils can all increase the amount of sediment yield.

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Implication of subsurface flow on rainfall-induced landslide: a case study

Cited by 53 publications

References 38 publications

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China

The Effects of Rainfall, Soil Type and Slope on the Processes and Mechanisms of Rainfall-Induced Shallow Landslides

The Effect of Rainfall, Soil Type and Slope on the Processes and Mechanism of Rainfall-Induced Landslide

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