Modeling shear stress distribution in natural small streams by soft computing methods

Genç, Onur; Kisi, Özgür; Ardıçlıoğlu, Mehmet

doi:10.15233/gfz.2016.33.11

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…To prevent flood damages at Kayseri area, the flow in Sarimsakli creek should be continuously monitored although it is an intermittent river. Sarimsakli creek is prone to several flash floods during the year, especially flash floods during the early summer season [26]. Fields measurements were obtained at Barsama station on Sarimsakli creek, which runs out to Kizilirmak river ( Fig.…”

Section: Field Measurementsmentioning

confidence: 99%

Calibration of channel roughness in intermittent rivers using HEC-RAS model: case of Sarimsakli creek, Turkey

Ardıçlıoğlu¹,

Kuriqi

2019

SN Appl. Sci.

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Natural channel's roughness determination constitutes one the most challenging procedure towards the development of hydraulic models for flood prediction and flood hazard mapping. Therefore, it is essential to calibrate and validate the channel's Manning's n roughness coefficient using simulation models. In this study, we calibrated and validated Manning's n roughness coefficient using HEC-RAS for Sarimsakli creek that is tributary of the Kizilirmak river in central Anatolia, Turkey. For calibration of Manning's n-values, six different flow regimes were considered based on mean daily flow records between 2005 and 2010. We found that calculated water surface profiles for different Manning's n are slightly lower than the measured one, that indicates a continuous underestimation of the roughness coefficient n by the model. Therefore, our results suggest that higher values of Manning's n should be considered in case of the intermittent rivers. A polynomial relationship was proposed between roughness values and Froude numbers. Finally, a linear relation was established between calculated and measured Manning's n roughness coefficient. Nevertheless, results showed that careful attention should be given to higher n-values because the differences between HEC-RAS and Manning's n becomes larger. Solution-oriented results obtained and the methodology applied to the Sarimsakli creek may serve as a practical reference for HEC-RAS modelling and flood prediction in intermittent rivers. Keywords Ecohydraulics • Flood risk • Flooding • Hydraulic model • Riparian vegetation • River restoration List of symbols HEC-RAS River Analysis System (RAS) developed by Hydrologic Engineering Center (HEC) of U.S. Army Corps of Engineers 1D/2D One dimensional/two dimensional n Manning's roughness coefficient (s/m 1/3) Q Flow discharge (m 3 s −1) V Flow velocity (m s −1) A Submerged cross-section's area (m 2) R Hydraulic radius (m) P Wetted perimeter (m) S ws Water surface slope (m/m) Fr Froude number (−) g Gravitational acceleration (ms −2) S Friction slope (m/m) ε Average difference (%) h meas Measured depth (m) h calc Calculated depth (m) S o Channel bottom slope (m/m) S f Friction slope (m/m) t Time (s) q l Lateral inflow (m 3 s −1) x Longitudinal coordinate (m)

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Section: Field Measurementsmentioning

confidence: 99%

Calibration of channel roughness in intermittent rivers using HEC-RAS model: case of Sarimsakli creek, Turkey

Ardıçlıoğlu¹,

Kuriqi

2019

SN Appl. Sci.

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Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis

Bahiuddin,

Mazlan,

Imaduddin

et al. 2024

Journal of the Mechanical Behavior of Materials

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Machine learning’s prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial–temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction.

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Modeling shear stress distribution in natural small streams by soft computing methods

Cited by 2 publications

References 34 publications

Calibration of channel roughness in intermittent rivers using HEC-RAS model: case of Sarimsakli creek, Turkey

Calibration of channel roughness in intermittent rivers using HEC-RAS model: case of Sarimsakli creek, Turkey

Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis

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