Predicting peak breach discharge due to embankment dam failure

2013 ◽  
Vol 15 (4) ◽  
pp. 1361-1376 ◽  
Author(s):  
Jasna Duricic ◽  
Tarkan Erdik ◽  
Pieter van Gelder
Keyword(s):  
Peak Flow ◽  
Numerical Models ◽  
The Other ◽  
Dam Failure ◽  
Embankment Dam ◽  
Loss Of Life ◽  
Flow Discharge ◽  
Proposed Model ◽  
Regression Techniques ◽  
Kriging Approach

Predicting peak breach discharge due to embankment dam failure is of vital importance for dam failure prevention and mitigation. Because, when dams fail, property damage is certain, but loss of life can vary depending on flood area and population. Many parametric breach models based on regression techniques have been developed so far. In this study, an efficient model is proposed to forecast peak discharge from the height of the water and volume of water behind the dam at failure, respectively, by using the Kriging approach. The previous studies, which consist of 13 numerical models, are used as a benchmark for testing the proposed new model, by employing five different error criteria. Moreover, a new database is compiled by extending the previous one. In addition, it is demonstrated that R2, which only quantifies the dispersion between measurements and predictions, should not be considered alone for checking the model capabilities. At least, the other criteria should be employed together with R2. As a result, it is shown that one can forecast the peak flow discharge with more significant accuracy by the proposed model than other previous models developed so far.

scholarly journals A GIS Tool for Mapping Dam-Break Flood Hazards in Italy

2019 ◽  
Vol 8 (6) ◽  
pp. 250 ◽  
Author(s):  
Raffaele Albano ◽  
Leonardo Mancusi ◽  
Jan Adamowski ◽  
Andrea Cantisani ◽  
Aurelia Sole
Keyword(s):  
Numerical Models ◽  
Technical Skills ◽  
Dam Break ◽  
Propagation Model ◽  
Dam Failure ◽  
Computational Time ◽  
Hazard Mapping ◽  
Flood Hazards ◽  
Proposed Model ◽  
Flood Prone Areas

Mapping the delineation of areas that are flooded due to water control infrastructure failure is a critical issue. Practical difficulties often present challenges to the accurate and effective analysis of dam-break hazard areas. Such studies are expensive, lengthy, and require large volumes of incoming data and refined technical skills. The creation of cost-efficient geospatial tools provides rapid and inexpensive estimates of instantaneous dam-break (due to structural failure) flooded areas that complement, but do not replace, the results of hydrodynamic simulations. The current study implements a Geographic Information System (GIS) based method that can provide useful information regarding the delineation of dam-break flood-prone areas in both data-scarce environments and transboundary regions, in the absence of detailed studies. Moreover, the proposed tool enables, without advanced technical skills, the analysis of a wide number of case studies that support the prioritization of interventions, or, in emergency situations, the simulation of numerous initial hypotheses (e.g., the modification of initial water level/volume in the case of limited dam functionality), without incurring high computational time. The proposed model is based on the commonly available data for masonry dams, i.e., dam geometry (e.g., reservoir capacity, dam height, and crest length), and a Digital Elevation Model. The model allows for rapid and cost-effective dam-break hazard mapping by evaluating three components: (i) the dam-failure discharge hydrograph, (ii) the propagation of the flood, and (iii) the delineation of flood-prone areas. The tool exhibited high accuracy and reliability in the identification of hypothetical dam-break flood-prone areas when compared to the results of traditional hydrodynamic approaches, as applied to a dam in Basilicata (Southern Italy). In particular, the over- and under-estimation rates of the proposed tool, for the San Giuliano dam, Basilicata, were evaluated by comparing its outputs with flood inundation maps that were obtained by two traditional methods whil using a one-dimensional and a two-dimensional propagation model, resulting in a specificity value of roughly 90%. These results confirm that most parts of the flood map were correctly classified as flooded by the proposed GIS model. A sensitivity value of over 75% confirms that several zones were also correctly identified as non-flooded. Moreover, the overall effectiveness and reliability of the proposed model were evaluated, for the Gleno Dam (located in the Central Italian Alps), by comparing the results of literature studies concerning the application of monodimensional numerical models and the extent of the flooded area reconstructed by the available historical information, obtaining an accuracy of around 94%. Finally, the computational efficiency of the proposed tool was tested on a demonstrative application of 250 Italian arch and gravity dams. The results, when carried out using a PC, Pentium Intel Core i5 Processor CPU 3.2 GHz, 8 GB RAM, required about 73 min, showing the potential of such a tool applied to dam-break flood mapping for a large number of dams.

scholarly journals Experimental and Numerical Investigation of 3D Dam-Break Wave Propagation in an Enclosed Domain with Dry and Wet Bottom

2021 ◽  
Vol 11 (12) ◽  
pp. 5638
Author(s):  
Selahattin Kocaman ◽  
Stefania Evangelista ◽  
Hasan Guzel ◽  
Kaan Dal ◽  
Ada Yilmaz ◽  
...  
Keyword(s):  
Wave Propagation ◽  
Numerical Models ◽  
Three Dimensional ◽  
Dam Break ◽  
Dam Failure ◽  
Navier Stokes ◽  
Negative Wave ◽  
Through Flow ◽  
Instantaneous Removal ◽  
Surface Patterns

Dam-break flood waves represent a severe threat to people and properties located in downstream regions. Although dam failure has been among the main subjects investigated in academia, little effort has been made toward investigating wave propagation under the influence of tailwater depth. This work presents three-dimensional (3D) numerical simulations of laboratory experiments of dam-breaks with tailwater performed at the Laboratory of Hydraulics of Iskenderun Technical University, Turkey. The dam-break wave was generated by the instantaneous removal of a sluice gate positioned at the center of a transversal wall forming the reservoir. Specifically, in order to understand the influence of tailwater level on wave propagation, three tests were conducted under the conditions of dry and wet downstream bottom with two different tailwater depths, respectively. The present research analyzes the propagation of the positive and negative wave originated by the dam-break, as well as the wave reflection against the channel’s downstream closed boundary. Digital image processing was used to track water surface patterns, and ultrasonic sensors were positioned at five different locations along the channel in order to obtain water stage hydrographs. Laboratory measurements were compared against the numerical results obtained through FLOW-3D commercial software, solving the 3D Reynolds-Averaged Navier–Stokes (RANS) with the k-ε turbulence model for closure, and Shallow Water Equations (SWEs). The comparison achieved a reasonable agreement with both numerical models, although the RANS showed in general, as expected, a better performance.

A Simple Constitutive Model for Prediction of Single-Peak Flow Curves Under Hot Working Conditions

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
Roxana Baktash ◽  
Hamed Mirzadeh
Keyword(s):  
Flow Stress ◽  
Strain Rate ◽  
Peak Flow ◽  
Rate Sensitivity ◽  
Peak Strain ◽  
Flow Curves ◽  
Hollomon Parameter ◽  
Proposed Model ◽  
Stress Coefficient ◽  
Hollomon Equation

The hot flow stress of a typical stainless steel was modeled by the Hollomon equation, a modified form of the Hollomon equation, and another modified form based on the Fields–Backofen equation. The coupled effect of the deformation temperature and strain rate was also taken into account in the proposed formulae by consideration of the Zener–Hollomon parameter or dependency of the constants on temperature. The modified Fields–Backofen equation was found to be appropriate for prediction of flow stress, in which the incorporation of peak strain and consideration of temperature dependencies of the strain rate sensitivity and the stress coefficient were found to be beneficial. Moreover, the simplicity of the proposed model justifies its applicability for expressing hot flow stress characterizing dynamic recrystallization (DRX).

scholarly journals Both Freud and Hoffman are Right: Anxious-Aggressive and Empathic Dimensions of Guilt

2008 ◽  
Vol 11 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Itziar Etxebarria ◽  
Pedro Apodaca
Keyword(s):  
Young Adults ◽  
Subjective Experience ◽  
Negative Emotions ◽  
The Other ◽  
Point Scale ◽  
Nested Models ◽  
Proposed Model ◽  
Better Than

The purpose of the study was to confirm a model which proposed two basic dimensions in the subjective experience of guilt, one anxious-aggressive and the other empathic, as well as another dimension associated but not intrinsic to it, namely, the associated negative emotions dimension. Participants were 360 adolescents, young adults and adults of both sexes. They were asked to relate one of the situations that most frequently caused them to experience feelings of guilt and to specify its intensity and that of 9 other emotions that they may have experienced, to a greater or lesser extent, at the same time on a 7-point scale. The proposed model was shown to adequately fit the data and to be better than other alternative nested models. This result supports the views of both Freud and Hoffman regarding the nature of guilt, contradictory only at a first glance.

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

scholarly journals JKRW Link Prediction – A New Ensemble Technique Based on Merging Other Known Techniques in The Social Network Analysis

2021 ◽  
Vol 15 (12) ◽  
pp. 125
Author(s):  
Aya Taleb ◽  
Rizik M. H. Al-Sayyed ◽  
Hamed S. Al-Bdour
Keyword(s):  
Link Prediction ◽  
Preferential Attachment ◽  
Large Data ◽  
The Other ◽  
Jaccard Coefficient ◽  
Ensemble Technique ◽  
Signed Rank ◽  
Proposed Model ◽  
Different Populations ◽  
A New Technique

In this research, a new technique to improve the accuracy of the link prediction for most of the networks is proposed; it is based on the prediction ensemble approach using the voting merging technique. The new proposed ensemble called Jaccard, Katz, and Random models Wrapper (JKRW), it scales up the prediction accuracy and provides better predictions for different sizes of populations including small, medium, and large data. The proposed model has been tested and evaluated based on the area under curve (AUC) and accuracy (ACC) measures. These measures applied to the other models used in this study that has been built based on the Jaccard Coefficient, Katz, Adamic/Adar, and Preferential attachment. Results from applying the evaluation matrices verify the improvement of JKRW effectiveness and stability in comparison to the other tested models.  The results from applying the Wilcoxon signed-rank method (one of the non-parametric paired tests) indicate that JKRW has significant differences compared to the other models in the different populations at <strong>0.95</strong> confident interval.

scholarly journals Streamflow estimation at partially gaged sites using multiple dependence conditions via vine copulas

2020 ◽  
Author(s):  
Kuk-Hyun Ahn
Keyword(s):  
Missing Values ◽  
The Other ◽  
Dependence Structure ◽  
Proposed Model ◽  
Pee Dee ◽  
Streamflow Estimation ◽  
Vine Copula ◽  
Bivariate Copula ◽  
Pee Dee River ◽  
Vine Copulas

Abstract. Reliable estimates of missing streamflow values are relevant for water resources planning and management. This study proposes a multiple dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin-Pee Dee River Basin, the eastern U. S. Both results inform that the proposed model is better suited for infilling missing values. After that, the performance of the vine copula is compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.

scholarly journals A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

2020 ◽  
Vol 17 (3) ◽  
pp. 849-865
Author(s):  
Zhongqin Bi ◽  
Shuming Dou ◽  
Zhe Liu ◽  
Yongbin Li
Keyword(s):  
State Of The Art ◽  
Weight Vector ◽  
User Preferences ◽  
The Other ◽  
Attention Networks ◽  
Proposed Model ◽  
Network Methods ◽  
Public Datasets ◽  
Novel Model ◽  
Attention Weight

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.

scholarly journals Distribution of the initial site of infection in diabetic foot and associated initiating etiologies

2021 ◽  
Vol 8 (11) ◽  
pp. 3320
Author(s):  
Joe Mathew ◽  
Rajeev S.
Keyword(s):  
Diabetic Foot ◽  
The Other ◽  
Common Condition ◽  
Cross Sectional ◽  
Site Specific ◽  
Loss Of Life ◽  
Factors Influencing ◽  
Male Patients ◽  
Nail Infection

Background: Diabetic foot is a very common condition responsible for a major bulk of surgical admissions and out-patient visits. Lack of awareness of many factors influencing the incidence of this disease complex has led to loss of life, limb and quality of life. The site-specific incidence of initial site and initiating factor has not been studied in diabetic foot.Methods: A study has been done over a period of one and a half years which looked into the distribution of initial site of infection in diabetic foot and associated initiating etiologies. The study was cross sectional. History, general inspection of foot, dermatological, neuropathic and vascular assessments were done, in addition to making careful notes about the site and cause of infection.Results:60.7% of patients were >60 years old, 55.3% were male patients. 63.3% of patients had diabetes for more than 10 years. In 29.3% the initial site of infection was the big toe, 22% in the ball of foot, 18% in the other 4 toes together, 14.7% in the interdigital spaces, 10.7% in the heel and 5.3% in the mid foot. In most of the cases the etiology is multifactorial, trauma in 56%, musculoskeletal deformities in 47.3%, callosities in 41.3%, cracks and fissures in 16.7%, fungal infection in 7.3%, nail infection in 6%, no initiating introduction of infection identified in 10.7%.Conclusions: Awareness of and thus particular stress being place on identification of specific site and cause of infection should help in care of the foot in diabetics.

A Framework for Homogeneous Cross-Project Defect Prediction

2021 ◽  
Vol 9 (1) ◽  
pp. 52-68
Author(s):  
Lipika Goel ◽  
Mayank Sharma ◽  
Sunil Kumar Khatri ◽  
D. Damodaran
Keyword(s):  
Class Imbalance ◽  
The Other ◽  
Training Data ◽  
Defect Prediction ◽  
Rank Test ◽  
Ensemble Model ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Proposed Model ◽  
Cross Project

Often, the prior defect data of the same project is unavailable; researchers thought whether the defect data of the other projects can be used for prediction. This made cross project defect prediction an open research issue. In this approach, the training data often suffers from class imbalance problem. Here, the work is directed on homogeneous cross-project defect prediction. A novel ensemble model that will perform in dual fold is proposed. Firstly, it will handle the class imbalance problem of the dataset. Secondly, it will perform the prediction of the target class. For handling the imbalance problem, the training dataset is divided into data frames. Each data frame will be balanced. An ensemble model using the maximum voting of all random forest classifiers is implemented. The proposed model shows better performance in comparison to the other baseline models. Wilcoxon signed rank test is performed for validation of the proposed model.

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