Estimation of manning roughness coefficient in alluvial rivers with bed forms using soft computing models

MB Yarahmadi, A Parsaie, M Shafai-Bejestan… - Water Resources …, 2023 - Springer
Flow conditions (flow discharge, flow depth, and flow velocity) in natural streams are mainly
determined via the flow resistance formula such as Manning's equation. Evaluating the …

Machine learning approaches for adequate prediction of flow resistance in alluvial channels with bedforms

AA Mir, M Patel - Water Science & Technology, 2024 - iwaponline.com
In natural rivers, flow conditions are mainly dependent on flow resistance and type of
roughness. The interactions among flow and bedforms are complex in nature as bedform …

Flow resistance and velocity distribution in a smooth triangular channel

H Mohammad Nezhad, M Mohammadi, A Ghaderi… - Water …, 2022 - iwaponline.com
This study investigates the flow resistance and velocity distribution in a smooth triangular
channel under varying slope conditions in a laboratory environment. For this purpose, two …

Insights into the prediction capability of roughness coefficient in current ripple bedforms under varied hydraulic conditions

K Roushangar, S Shahnazi - Journal of Hydroinformatics, 2021 - iwaponline.com
Ubiquitous flow bedforms such as ripples in rivers and coastal environments can affect
transport conditions as they constitute the bed roughness elements. The roughness …

Developing Extended and Unscented Kalman Filter-Based Neural Networks to Predict Cluster-Induced Roughness in Gravel Bed Rivers

M Karbasi, M Ghasemian, M Jamei, A Malik… - Water Resources …, 2024 - Springer
Flow resistance in natural gravel-bed rivers must be precisely predicted in order for water-
related infrastructure to be designed effectively. Cluster microforms are significant factors in …

The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river

K Roushangar, N Aghajani… - Journal of …, 2021 - iwaponline.com
Sediment transport is one of the most important issues in river engineering. In this study, the
capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river …

Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

K Amininia, SM Saghebian - Journal of Hydroinformatics, 2021 - iwaponline.com
HIGHLIGHTS Kernel extreme learning machine (KELM) and multivariate adaptive
regression splines (MARS) approaches were used for MRF modeling in three successive …

[HTML][HTML] Modeling flow resistance and geometry of dunes bed form in alluvial channels using hybrid RANN–AHA and GEP models

R Ezzeldin, M Abd-Elmaboud - International Journal of Sediment Research, 2024 - Elsevier
Dunes formation in sandy rivers significantly impacts flow resistance, subsequently affecting
water levels, flow velocity, river navigation, and hydraulic structures performance. Accurate …

Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches

R Ghasempour, K Roushangar, P Sihag - Water Supply, 2021 - iwaponline.com
Sediment transportation and accurate estimation of its rate is a significant issue for river
engineers and researchers. In this study, the capability of kernel based approaches …

A new formula for predicting movable bed roughness coefficient in the Middle Yangtze River

X Liu, J Xia, M Zhou, S Deng… - Progress in Physical …, 2022 - journals.sagepub.com
Computing movable bed roughness plays an important role in the modeling of flood routing
and bed deformation, and the magnitude of movable bed roughness is closely associated …