Developing an Efficient Auto-Calibration Algorithm for HEC-HMS Program

Dariane, Alireza B.; Javadianzadeh, Mohamad M.; James, L. Douglas

doi:10.1007/s11269-016-1260-7

Cited by 24 publications

(14 citation statements)

References 22 publications

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“…In these models, the NSE rarely reach to satisfying value for model evaluation. The results of the current study revealed satisfying results of employing NSE index in line with other researches(Dariane and Javadianzadeh ; Dariane et al 2016).…”

Section: R a F T 1 Introductionsupporting

confidence: 90%

Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

et al. 2017

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The present study investigates the effectiveness of evolutionary algorithms such as genetic algorithm (GA) evolved neural network in estimating roller compacted concrete pavement (RCCP) characteristics including flexural and compressive strength of RCC and also energy absorbency of mixes with different compositions. A real coded GA was implemented as training algorithm of feed forward neural network to simulate the models. The genetic operators were carefully selected to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm neural network model, Nash-Sutcliffe efficiency criterion was employed and also utilized as fitness function for genetic algorithm which is a different approach for fitting in this area. The results showed that the GA-based neural network model gives a superior modeling. The well-trained neural network can be used as a useful tool for modeling RCC specifications.

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Section: R a F T 1 Introductionsupporting

confidence: 90%

Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

et al. 2017

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“…Optimization algorithm is fundamental for model calibration, different objective functions, and search algorithm which may lead to significantly different parameter values (Dariane et al, 2016). There are numerous factors affecting an algorithm considered as the optimization one (Arsenault et al, 2014) and hence different algorithms at various locations were compared.…”

Section: Uncertainty From Model Setupmentioning

confidence: 99%

Quantifying Uncertainty of Probable Maximum Flood

Singh

2021

J. Hydrol. Eng.

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Probable maximum flood (PMF) has been used in large hydraulic infrastructure design for decades. However, a complete framework for deriving PMF with uncertainty analysis is lacking. This study investigated from probable maximum precipitation (PMP) with uncertainty to probable maximum storm (PMS), rainfall-runoff (R-R) model with uncertainty, and PMF with uncertainty. A basin-scale model, based on the Hershfield method, was developed for calculating PMP and uncertainty. It showed that PMP and uncertainty from the proposed method were more reliable than other statistical methods because of less restrictive assumptions and the improvement in methodology. The average improvements in terms of the difference percentages for 1-hour, 6-hour and 24hour duration PMPs were 53.84%, 81.04% and 72.60%, respectively. The improvements in uncertainty by the Delta method were 15.54%, 9.71%, 8.93%, respectively. Hydrometeorological Report No. 52 (HMR 52) with updated data was adopted to design the PMS from PMP. The updated storm records indicated a different within/without-storm depth-area relation (WWSDA) than HMR 52. The PMP values with 95% confidence interval (CI) were transferred to PMSs utilizing the DAD maps and the WWSDA relations. With the GLUE methodology, the impacts of different sources of uncertainty on the R-R model performance and uncertainty were investigated. Results showed that the uncertainties were more sensitive to smaller basin area, average area of subbasin, precipitation intensity, and observed peak flow, which means that for extreme events modeling with opposite basin features, the selection of model input and model setup had iii less impact on model performance and uncertainty. PMSs were then routed by the R-R model with uncertainty to obtain the PMF with uncertainty. Results showed that in lower Brazos River basin, the PMF peak flow derived by the average PMP value was 12716 CMS, which was around three times the peak flow from hurricane Harvey (3764 CMS).

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“…This model addresses two important processes in the transformation of P e into DSR (CLARK, 1945): attenuation, which takes into account the reduction of streamflows generated by P e due to storage in the watershed and translation, which considers the time lag between P e in the watershed and its contribution to streamflow at the outlet. According to Che, Nangare and Mays (2014), the model requires the definition of the following parameters: time of concentration (t c ) of the watershed and storage coefficient (R).…”

Section: Clark's Instantaneous Unit Hydrographmentioning

confidence: 99%

“…Future climate projections and soil management changes must be taken into account in order to get a sustainable watershed management. Accordingly, rainfall-runoff models are broadly used worldwide for a number of applications including flood forecasting and hydraulic structure conceiving (DARIANE;JAVADIANZADEH;JAMES, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…time-area histograms, time of concentration and storage (AHMAD; GHUMMAN; AHMAD, 2009; SADEGHI; ASADI 2010; RIVARD; LEFEBVRE; PARADIS, 2014). Scientific studies have demonstrated the potential of Clark's IUH to estimate watershed floods around the world (SILVA; WEERAKOON;HERATH, 2014;DU et al, 2015;DARIANE;JAVADIANZADEH;JAMES, 2016;BESKOW et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Spatial discretization influence on flood modeling using unit hydrograph theory

Steinmetz

Beskow

Terra

et al. 2019

RBRH

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The Unit Hydrograph (UH) is the most popular method for flood related applications. There are several conceptual and geomorphological models based on UH and coupled with different spatial discretizations. However, there are few studies concerning the evaluation of UH models according to semi-distributed approaches. This study aimed to assess the influence of lumped and semi-distributed modeling on the applicability of Soil Conservation Service UH (SCS UH) and Clark’s Instantaneous UH (Clark’s IUH) for estimation of flood hydrographs. The methodological procedures were conducted in the Hydrological Modeling System (HEC-HMS) using rainfall-runoff events of a gauged watershed in Southern Brazil. The main conclusions were: a) CIUH under the semi-distributed approach provided slightly superior performance; b) CIUH was able to effectively estimate the direct surface runoff hydrographs even for long duration rainfall events; c) SCS UH presented more accurate hydrographs for lumped modeling; d) the Nelder and Mead algorithm may have limited application.

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Developing an Efficient Auto-Calibration Algorithm for HEC-HMS Program

Cited by 24 publications

References 22 publications

Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

Determining optimal combination of roller compacted concrete pavement mixture containing recycled asphalt pavement and crumb rubber using hybrid artificial neural network–genetic algorithm method considering energy absorbency approach

Quantifying Uncertainty of Probable Maximum Flood

Spatial discretization influence on flood modeling using unit hydrograph theory

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