Application of copula method and neural networks for predicting peak outflow from breached embankments

Hooshyaripor, Farhad; Tahershamsi, Ahmad; Golian, Saeed

doi:10.1016/j.jher.2013.11.004

Cited by 45 publications

(15 citation statements)

References 29 publications

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“…H w was used as single independent variable if the location was close to dam breach whereas both H w and V w were considered for distant locations from dam breach. A robust artificial neural network was developed to predict peak discharge from original dataset which underestimated the extreme values, thus synthetic dataset was generated using copula method to improve the efficiency of the model (Hooshyaripor et al 2014). Another dimensionally homogeneous model was proposed and was found superior in terms of root-mean-square error and efficiency coefficient (Azimi et al 2015).…”

Section: Empirical Relationsmentioning

confidence: 99%

Review of moraine dam failure mechanism

Neupane

Chen

Cao

2019

Geomatics, Natural Hazards and Risk

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Moraine dams, impounding glacial lakes, are among the weak natural dams because of their slope, freeboard and composition. Glacial lake outburst floods occurring due to the failure of moraine dams are significant hazards for the valley downstream the failure, as they possess huge amount of hydraulic energy that can kill thousands and destroy infrastructures and riverine landscape. Also, the entrainment of debris to the flow from the breaching process and downstream channel may develop the flow into a much bigger disaster or even a series of hazard chain. Ice and rock avalanche or landslide, glacier calving, degradation of ice cores, earthquake and atmospheric events trigger the breaching phenomenon which generates a series of waves overtopping the dam or seepage causing the failure of the dam. Various approaches have been discussed in this review paper to produce an understanding of the failure mechanism of moraine dams: experimental works, empirical relationships, analytical solutions, and numerical modelling. No concrete experimental investigations and parametric solutions pertained to the failure mechanism of moraine dam is found in the review, but various empirical relationships are discussed and suitable approaches for numerical modelling are suggested as per the requirement of the task. ARTICLE HISTORY

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Section: Empirical Relationsmentioning

confidence: 99%

Review of moraine dam failure mechanism

Neupane

Chen

Cao

2019

Geomatics, Natural Hazards and Risk

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“…In his study the input and output data were normalized (0 and 1) and the impact of dimensionless and dimensional inputs in modeling the scour depth was investigated. There were only a few later application of ANN models due to many critical disadvantages as the low speed convergence and poor generalization power (Choubin et al, 2018;Hooshyaripor et al, 2014). Also, the performances of the ANN model strongly depend of the extension of the dataset (Hooshyaripor and Tahershamsi, 2013).…”

Section: Introductionmentioning

confidence: 99%

A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction

Khosravi

Khozani

Mao

2021

Journal of Hydrology

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Complex vortex flow patterns around bridge piers, especially during floods, cause scour process that can result in the failure of foundations. Abutment scour is a complex three-dimensional phenomenon that is difficult to predict especially with traditional formulas obtained using empirical approaches such as regressions. This paper presents a test of a standalone Kstar model with five novel hybrid algorithm of bagging (BA-Kstar), dagging (DA-Kstar), random committee (RC-Kstar), random subspace (RS-Kstar), and weighted instance handler wrapper (WIHW-Kstar) to predict scour depth (ds) for clear water condition. The dataset consists of 99 scour depth data from flume experiments (Dey and Barbhuiya, 2005) using abutment shapes such as vertical, semicircular and 45° wing. Four dimensionless parameter of relative flow depth (h/l), excess abutment Froude number (Fe), relative sediment size (d50/l) and relative submergence (d50/h) were considered for the prediction of relative scour depth (ds/l). A portion of the dataset was used for the calibration (70%), and the remaining used for model validation.Pearson correlation coefficients helped deciding relevance of the input parameters combination and finally four different combinations of input parameters were used. The performance of the models was assessed visually and with quantitative metrics. Overall, the best input combination for vertical abutment shape is the combination of Fe, d50/l and h/l, while for semicircular and 45° wing the combination of the Fe and d50/l is the most effective input parameter combination. Our results show that incorporating Fe, d50/l and h/l lead to higher performance while involving d50/h reduced the models prediction power for vertical abutment shape and for semicircular and 45° wing involving h/l and d50/h lead to more error. The WIHW-Kstar provided the highest performance in scour depth prediction around vertical abutment shape while RC-Kstar model outperform of other models for scour depth prediction around semicircular and 45° wing.

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“…Sometimes, this concern dose not lead to providing the accurate prediction for scour depth. Consequently, the equations that are extended based on these methods cannot be applicable in most cases (Hooshyaripor, Tahershamsi, & Golian, 2014). Hence, developing new techniques for modifying traditional physical-based analysis is crucial.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Riahi-Madvar

Dehghani

Seifi

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R 2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R 2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.

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Application of copula method and neural networks for predicting peak outflow from breached embankments

Cited by 45 publications

References 29 publications

Review of moraine dam failure mechanism

Review of moraine dam failure mechanism

A comparison between advanced hybrid machine learning algorithms and empirical equations applied to abutment scour depth prediction

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

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