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31.
Forecasting flood risk in the Indus River system using hydrological parameters and its damage assessment 总被引：1，自引：1，他引：0
Hydrological parameters are among the widely used parameters in assessing flood risk. On the other hand, anticipated flood damages, in case of flooding, are estimated with the help of expected losses in areas nearer to the watercourse. The major source of almost every-year flooding in Pakistan is the Indus River system that comprises the major rivers of Pakistan. We first use observed data to construct simulated data models based on various probability distributions namely normal, lognormal, Weibull, largest extreme value, gamma-3, and log-Pearson type-3 distributions and thereby compute probable maximum flood. Secondly, we perform log-Pearson type-3 analysis with and without historic adjustment on the observed data series of 17 years to forecast floods with return periods T of 2, 5, 10, 25, 50, 100, and 200 years. We also categorize the river structures based on the risk of flooding. Lastly, we estimate risk of flood damages in terms of expected losses based on observed data. The present study reveals that the log-Pearson type-3 distribution is relatively better for estimating probable maximum flood. We use exceedence probability to assess the risk of flooding in the various structures of the said rivers. The analysis shows that flood damages in Pakistan may be reduced by increasing the design capacity of the structures and also by giving awareness to people about the flood-generating factors. 相似文献
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Sadra Shadkani Akram Abbaspour Saeed Samadianfard Sajjad Hashemi Amirhosein Mosavi Shahab S.Band 《国际泥沙研究》2021,36(4):512-523
Monitoring sediment transport is essential for managing and maintaining rivers.Estimation of the sediment load in rivers is fundamental for the study of sediment movement,erosion,and flood control.In the current study,three machine learning models-multi-layer perceptron(MLP),multi-layer perceptron-stochastic gradient descent(MLP-SGD),and gradient boosted tree(GBT)-were utilized to estimate the suspended sediment load(SSL)at the St.Louis(SL)and Chester(CH)stations on the Mississippi River,U.S.Four evaluation criteria including the Correlation Coefficient(CC),Nash Sutcliffe Efficiency(NSE),Scatter Index(SI),and Willmott’s Index(WI)were utilized to evaluate the performance of the used models.A sensitivity analysis of the models to the input variables revealed that the current day discharge variable had the most effect on the SSL at both stations,but in the absence of current-day discharge data(Qt),a combination of input parameters including SSLt-3,SSLt-2,SSLt-1,Qt-3,Qt-2,Qt-1 can be used to estimate the SSL.The comparative outcomes indicated the high accuracy of MLP-SGD-5 model with a CC of 0.983,SI of 0.254,WI of 0.991,and NSE of 0.967 at station CH and the MLP-SGD-6 model with a CC of 0.933,SI of 0.576,WI of 0.961,and NSE of 0.867,respectively,at station SL.The results of MLP models were improved by SGD optimization.Therefore,the MLP-SGD method is recommended as the most accurate model for SSL estimation. 相似文献
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A temporal artificial neural network‐based model is developed and applied for long‐lead rainfall forecasting. Tapped delay lines and recurrent connections are two different components that are used along with a static multilayer perceptron network to design a time‐delay recurrent neural network. The proposed model is, in fact, a combination of time‐delay and recurrent neural networks. The model is applied in three case studies of the Northwest, West, and Southwest basins of Iran. In addition, an autoregressive moving average with exogenous inputs (ARMAX) model is used as a baseline in order to be compared with the time‐delay recurrent neural networks developed in this study. Large‐scale climate signals, such as sea‐level pressure, that affect the rainfall of the study area are used as the predictors in the models, as well as the persistence between rainfall data. The results of winter‐spring rainfall forecasts are discussed thoroughly. It is demonstrated that in all cases the proposed neural network results in better forecasts in comparison with the statistical ARMAX model. Moreover, it is found that in two of three case studies the time‐delay recurrent neural networks perform better than either recurrent or time‐delay neural networks. The results demonstrate that the proposed method can significantly improve the long‐lead forecast by utilizing a non‐linear relationship between climatic predictors and rainfall in a region. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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Evaluating the seismic active earth pressure on retaining walls is currently based on pseudo-static method in practices. In this method, however, it is not simple, choosing an appropriate value for earthquake coefficient, which should fully reflect the dynamic characteristics of both soil and loading is an important problem. On the other hand, by using only two extra dynamic parameters that are shear wave velocity of soil and predominant frequency of probable earthquake, one can benefit from another more accurate tool called pseudo-dynamic method to solve the problem of earth pressure.In this study in the framework of limit equilibrium analysis, pseudo-dynamic method has been applied into horizontal slice method of analysis to account for the effect of earthquake on lateral earth pressure history behind rigid retaining walls. The pressure history resulted from a number of analyses shows that before and after reaching the peak resultant force, different pressure distributions occur behind a wall that put more local pressure than the same at peak. This method would be a tool to control this phenomenon in wall design. 相似文献
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Mohammed Y. Fattah Bushra S. Zbar Faris S. Mustafa 《Marine Georesources & Geotechnology》2017,35(8):1111-1120
In this study, the dynamic response of pile foundation in dry sandy soil excited by two opposite rotary machines was considered experimentally. A small scale physical model was manufactured to accomplish the experimental work in the laboratory. The physical model consists of two small motors supplied with eccentric mass (0.012?kg) and eccentric distance (20?mm) representing the two opposite rotary machines, an aluminum shaft as the pile, and a steel plate a pile cap. The experimental work was achieved taking the following parameters into considerations: pile embedment depth ratio (L/d, where L is the pile length and d is its diameter) and operating frequency of the rotary machines. All tests were conducted in medium dense fine sandy soil with 60% relative density. Twelve tests were performed to measure the change in load transferred through the pile’s tip to the underlying soil. To predict precisely the dynamic load that will be induced from the rotary machines, a mini load cell with a capacity of 100?kg was mounted between the aluminum plate (the machine base) and the steel plate (pile cap). The results revealed that, before machine operation, the pile tip load was approximately equal to the static load (machine and pile cap), whereas during machines’ operation, the pile tip load decreased for all embedment depth ratios and operating frequencies. This reduction was due to the action of skin friction that was mobilized along the pile during operation, and as a result the factor of safety against pile bearing failure increases. For all operating frequencies and pile lengths, the factor of safety against bearing failure increased during machines’ operation, where the pile tip load became less than its value before starting operation. During operation, the skin friction resistance mobilized along pile length led to decrease the bearing load. 相似文献