Accurately predicting the flow of water during a dam break is crucial for managing and responding to potential flood disasters. Although the characteristics of a dam break wave after a complete dam break have been inv...
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Accurately predicting the flow of water during a dam break is crucial for managing and responding to potential flood disasters. Although the characteristics of a dam break wave after a complete dam break have been investigated extensively, studies on partial dam break processes have been rare. To this end, this paper proposed a hydraulic model for partial dam breaks based on the water flow through a sluice gate. Five different hydraulic models using 459 datasets of sluice gate flows from both experimental tests and the literature were evaluated. A stepwise algorithm was developed by combining the sluice gate flow model with the law of mass conservation;this algorithm was used to develop a predictive model for the flow and level of water during partial dam breaks. Based on the water-level data obtained from partial dam break simulations, the hydraulic model was improved by introducing time and submergence correction coefficients. Compared with the experimental results, the mean absolute percentage error of the corrected model was 1.572 %, indicating a high prediction accuracy. Consequently, the proposed model can provide important technical support for managing partial dam break.
In this article we consider the problem of building a linear prediction model when the number of candidate predictors is large and the data possibly contain anomalies that are difficult to visualize and clean. We want...
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In this article we consider the problem of building a linear prediction model when the number of candidate predictors is large and the data possibly contain anomalies that are difficult to visualize and clean. We want to predict the nonoutlying cases;therefore, we need a method that is simultaneously robust and scalable. We consider the stepwise least angle regression (LARS) algorithm which is computationally very efficient but sensitive to outliers. We introduce two different approaches to robustify LARS. The plug-in approach replaces the classical correlations in LARS by robust correlation estimates. The cleaning approach first transforms the data set by shrinking the outliers toward the bulk of the data (which we call multivariate Winsorization) and then applies LARS to the transformed data. We show that the plug in approach is time-efficient and scalable and that the bootstrap can be used to stabilize its results. We recommend using bootstrapped robustified LARS to sequence a number of candidate predictors to form a reduced set from which a more refined model can be selected.
Over the past years, the health impact of airborne particulate matter has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ...
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Over the past years, the health impact of airborne particulate matter has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient concentrations. In this paper, we are interested in the statistical classification of the daily mean concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods;the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of concentration may be crucial to get an accurate prediction.
This paper proposed an improved stepwise algorithm to simulate the grout diffusion in a single fracture considering the dynamic grouting parameter boundary condition and the spatial- and time-dependent viscosity of th...
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This paper proposed an improved stepwise algorithm to simulate the grout diffusion in a single fracture considering the dynamic grouting parameter boundary condition and the spatial- and time-dependent viscosity of the grout. The method was more effective and could result in variation in key output parameters at any measurement point/time (in space and time). Based on the algorithm, three types of dynamic pressure boundary conditions, which are more applicable in grouting engineering practice, were designed to illustrate the grouting process. Compared with constant pressure grouting, the dynamic adjustments of pressure grouting were found to be beneficial to grout propagation in most cases. Some other factors were also studied under dynamic pressure boundary conditions, such as the spatiotemporal variation in the slurry viscosity and fracture aperture, which demonstrate a significant influence on the grout migration. Finally, a dynamic pressure grouting system was prepared, and the accuracy of the algorithm was successfully validated using a series of laboratory tests.
Accurate prediction of the spread of fluids leaking from underground pipelines is crucial for risk assessment. In this study, we developed a method for predicting the diffusion range of pipeline seepage fluids within ...
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Accurate prediction of the spread of fluids leaking from underground pipelines is crucial for risk assessment. In this study, we developed a method for predicting the diffusion range of pipeline seepage fluids within an unsaturated stratum. First, we derived a seepage-diffusion equation based on the generalized Darcy's law and subsequently analyzed the mechanism of fluid diffusion in the unsaturated stratum. We then constructed a model for the seepage-diffusion of a pipeline leakage fluid, incorporating variables such as the saturation, permeability coefficient, diffusion pressure, and diffusion distance of soil microelements across various time intervals, using a stepwise algorithm in tandem with the Green-Ampt model and VG-Mualen permeability coefficient function. We investigated the influence of fluid self-gravity, saturated permeability coefficient, initial saturation, and intrapipe pressure on the diffusion distance of the pipeline leakage fluid in an unsaturated stratum, focusing on specific cases. The results indicate that an increase in the saturated permeability coefficient, initial saturation, and intra-pipe pressure leads to an increase in the fluid diffusion distance. A simulation test was conducted to validate the proposed seepage-diffusion model. The findings of this study can be employed to predict the diffusion range of pipeline leakage fluids in various formation types, providing a vital foundation for pipeline leakage accident management.
In this paper a Bayesian method is proposed to estimate dynamic origin-destination (O-D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning...
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In this paper a Bayesian method is proposed to estimate dynamic origin-destination (O-D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O-D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O-D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O-D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O-D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen-Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O-D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O-D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and "true" O-D demands is relatively small, and the O-D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O-D demands with fine accuracy.
Trochlear palsy often results from traumatic, congenital and microvascular disorders. An intra-axial lesion as a cause of trochlear palsy is uncommon. Moreover, it usually accompanies other neurological deficits. Isol...
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Trochlear palsy often results from traumatic, congenital and microvascular disorders. An intra-axial lesion as a cause of trochlear palsy is uncommon. Moreover, it usually accompanies other neurological deficits. Isolated trochlear palsy as the only presentation of brainstem stroke is unexpected. This current case report describes a 74-year-old male that presented with trochlear palsy without other neurological signs. Brain magnetic resonance imaging (MRI) revealed an acute midbrain infarction. The case report also reviews recent literature and provides a stepwise algorithm for clinicians to approach patients with trochlear palsy. Despite its rarity, clinicians are advised to consider ischaemic stroke as a cause of trochlear palsy even without other neurological deficits. Early MRI should be performed for prompt and proper management.
This paper presents the development of the fusion model skills diagnosis system (fusion model system), which can help integrate standardized testing into the learning process with both skills-level examinee parameters...
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