Experimental Study on Dam-Break-Like Tsunami Surge Impacts on Small Balls Climbing on Different Slopes

2020 ◽  
Vol 14 (06) ◽  
pp. 2050022
Author(s):  
Cheng Chen ◽  
Fangyu Wang ◽  
Hui Lin
Keyword(s):  
Experimental Study ◽  
Dam Break ◽  
Strength Increase ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Tsunami Inundation ◽  
Positive Effects ◽  
Ball Diameter ◽  
Surface Profiles ◽  
Inundation Height

An experimental study was carried out to investigate the tsunami surge impacts on small balls climbing on different slopes. Dam-break flows were generated in a flume to simulate tsunami surge. The water surface profiles at the sluice gate were observed, the tsunami inundation height and the surge velocity in the flume were measured, and the maximum climbing heights of small balls on different slopes were recorded. Results show that the dam-break speed and the tsunami surge strength increase with increasing reservoir water level. The increasing tsunami inundation height, the decreasing ball density, and the decreasing ball diameter have positive effects on the maximum ball climbing height. Based on the normalized experimental data, equations for estimating the maximum ball climbing heights on different slopes were proposed as functions of the inundation height, the ball diameter, and the ball density. The calculated values from the equation are generally within [Formula: see text]% of the measured values in the experimental ranges.

scholarly journals The Monitoring-Based Analysis on Deformation-Controlling Factors and Slope Stability of Reservoir Landslide: Hongyanzi Landslide in the Southwest of China

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Bing Han ◽  
Bin Tong ◽  
Jinkai Yan ◽  
Chunrong Yin ◽  
Liang Chen ◽  
...  
Keyword(s):  
Water Level ◽  
Water Pressure ◽  
Monitoring Program ◽  
Significant Risk ◽  
Controlling Factors ◽  
Seepage Water ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Area ◽  
Reservoir Landslide

Reservoir landslide is a type of commonly seen geological hazards in reservoir area and could potentially cause significant risk to the routine operation of reservoir and hydropower station. It has been accepted that reservoir landslides are mainly induced by periodic variations of reservoir water level during the impoundment and drawdown process. In this study, to better understand the deformation characters and controlling factors of the reservoir landslide, a multiparameter-based monitoring program was conducted on a reservoir landslide—the Hongyanzi landslide located in Pubugou reservoir area in the southwest of China. The results indicated that significant deformation occurred to the landslide during the drawdown period; otherwise, the landslide remained stable. The major reason of reservoir landslide deformation is the generation of seepage water pressure caused by the rapidly growing water level difference inside and outside of the slope. The influences of precipitation and earthquake on the slope deformation of the Hongyanzi landslide were insignificant.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

scholarly journals Characterizing the Development Pattern of a Colluvial Landslide Based on Long-Term Monitoring in the Three Gorges Reservoir

2021 ◽  
Vol 13 (2) ◽  
pp. 224
Author(s):  
Xin Liang ◽  
Lei Gui ◽  
Wei Wang ◽  
Juan Du ◽  
Fei Ma ◽  
...  
Keyword(s):  
Time Variable ◽  
Development Pattern ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Three Gorges ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Colluvial Landslide

Since the impoundment of the Three Gorges Reservoir (TGR) in June 2003, the fluctuation of the reservoir water level coupled with rainfall has resulted in more than 2500 landslides in this region. Among these instability problems, most colluvial landslides exhibit slow-moving patterns and pose a significant threat to local people and channel navigation. Advanced monitoring techniques are therefore implemented to investigate landslide deformation and provide insights for the subsequent countermeasures. In this study, the development pattern of a large colluvial landslide, locally named the Ganjingzi landslide, is analyzed on the basis of long-term monitoring. To understand the kinematic characteristics of the landslide, an integrated analysis based on real-time and multi-source monitoring, including the global navigation satellite system (GNSS), crackmeters, inclinometers, and piezometers, was conducted. The results indicate that the Ganjingzi landslide exhibits a time-variable response to the reservoir water fluctuation and rainfall. According to the supplement of community-based monitoring, the evolution of the landslide consists of three stages, namely the stable stage before reservoir impoundment, the initial movement stage of retrogressive failure, and the shallow movement stage with stepwise acceleration. The latter two stages are sensitive to the drawdown of reservoir water level and rainfall infiltration, respectively. All of the monitoring approaches used in this study are significant for understanding the time-variable pattern of colluvial landslides and are essential for landslide mechanism analysis and early warning for risk mitigation.

scholarly journals Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021 ◽  
Vol 11 (4) ◽  
pp. 1381
Author(s):  
Xiuzhen Li ◽  
Shengwei Li
Keyword(s):  
Support Vector Regression ◽  
Water Level ◽  
Large Scale ◽  
Displacement Rate ◽  
Support Vector ◽  
Single Factor ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Depth Analysis ◽  
Three Factor

Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.

Experimental study and numerical verification of silted-up dam break

2020 ◽  
Vol 590 ◽  
pp. 125267
Author(s):  
Foad Vosoughi ◽  
Gholamreza Rakhshandehroo ◽  
Mohammad Reza Nikoo ◽  
Mojtaba Sadegh
Keyword(s):  
Experimental Study ◽  
Dam Break ◽  
Numerical Verification

Seasonal succession and long-term stability of pelagic community in a productive reservoir

2005 ◽  
Vol 56 (8) ◽  
pp. 1137 ◽  
Author(s):  
V. F. Matveev ◽  
L. K. Matveeva
Keyword(s):  
Time Series ◽  
Water Level ◽  
Seasonal Succession ◽  
Perca Fluviatilis ◽  
Taxonomic Composition ◽  
Stationary Time Series ◽  
Introduced Fish ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Murray River

In Lake Hume, a reservoir located in an active agricultural zone of the Murray River catchment, Australia, time series for the abundances of phytoplankton and zooplankton taxa, monitored from 1991 through to 1996, were stationary (without trends), and plankton taxonomic composition did not change. This indicated ecosystem resilience to strong fluctuations in reservoir water level, and to other potential agricultural impacts, for example eutrophication and pollution. Although biological stressors such as introduced fish and invertebrate predators are known to affect planktonic communities and reduce biodiversity in lakes, high densities of planktivorous stages of alien European perch (Perca fluviatilis) and the presence of carp (Cyprinus carpio) did not translate into non-stationary time series or declining trends for plankton in Lake Hume. However, the seasonal successions observed in the reservoir in different years did not conform well to the Plankton Ecology Group (PEG) model. Significant deviations of the Lake Hume successional pattern from the PEG model included maxima for phytoplankton abundance being in winter and the presence of a clear water phase without large zooplankton grazers. The instability of the water level in Lake Hume probably causes the dynamics of most planktonic populations to be less predictable, but did not initiate the declining trends that have been observed in some other Australian reservoirs. Both the PEG model and the present study suggest that hydrology is one of the major drivers of seasonal succession.

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