Hydraulic model tests for propagation of flow and sediment in floods due to breaking of a natural landslide dam during a mountainous torrent     

Takahiro Itoh  Akihiko Ikeda  Takahiko Nagayama  Takahisa Mizuyama 《国际泥沙研究》2018,33(2):107-116

During mountain torrents, large-magnitude floods may result from heavy rainfall and cause the breakage of landslide dams naturally formed by heavy rainfall, earthquakes, and so on. The characteristics of longitudinal spreading of clear water discharge and changes in flow depth must be clarified because the changes in peak depth have not yet been examined in steep-slope torrents and because there are few data on spreading of flash floods and related sedimentation in mountainous torrents. In the present study, experimental data were collected through hydraulic model tests over a rigid bed, and the spreading of water, fine sediment, bed load, and large boulders due to flooding are discussed assuming that flash flooding/debris flows occur in the upstream reach. The effects of changes in flow width, such as expansions and contractions in the flow width, as well as changes in meandering channels, sediment transportation, and spreading flow depth resulting from bores are examined using flume data for a steep-slope torrent. The data obtained in the present study reveal that fine sediment components are transported to the downstream reach if large-magnitude floods occur and that the spreading rate and peak lags of the fine sediment and water level indicate the occurrence of a flood in the upstream reach.  相似文献   

Laboratory study on submarine debris flow     

Mei Yin 《Marine Georesources & Geotechnology》2018,36(8):950-958

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Multi-hazard susceptibility mapping based on Convolutional Neural Networks     

《地学前缘(英文版)》2022,13(5):101425

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.  相似文献   

Contrasting sediment gravity flow processes in the late Llandovery,Rhuddnant Grits turbidite system,Welsh Basin     

Chris J. Clayton 《Geological Journal》1994,29(2):167-181

The Rhuddnant Grits turbidite system was deposited within an elongate, fault-bounded trough in the late Llandovery (Telychian) Welsh Basin. Two groups of sandstones are identified within the system: high-matrix sandstones and laminated sandstones. The high-matrix sandstones are medium to very thick bedded, fine to very coarse-grained muddy sandstones. The high-matrix sandstone beds are almost entirely structureless and have several features indicative of deposition from high density turbidity currents, probably undergoing late stage debris flow behaviour (e.g. grain size discontinuities, inverse grading, floating clasts). The laminated sandstones are thin to very thin bedded, fine-grained and have a distinctive mud/silt lamination. Tractional structures and convolution are common in these beds. They were probably deposited by slow moving, dilute turbidity currents. Dissimilar palaeocurrent vectors and estimates of flow properties from the two types of sandstone support the contrasting nature of the depositing flows. A coarsening and thickening upwards trend is identified in the laminated sandstones of the Rhuddnant Grits Formation. This trend is not reflected in the high-matrix sandstone beds. Although the high-matrix sandstones appear in packets or groups within the laminated sandstone background, they were otherwise deposited in an entirely random manner throughout the exposed system. This may suggest that the two types of sandstone are the result of different triggering mechanisms at source, or of contrasting flow properties developed early in the flow histories.  相似文献