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Related Concept Videos

Gradually Varying Flow01:29

Gradually Varying Flow

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Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
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Rapidly Varying Flow01:24

Rapidly Varying Flow

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Design Example: Design of an Irrigation Channel01:27

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Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Application of novel artificial bee colony optimized ANN and data preprocessing techniques for monthly streamflow

Okan Mert Katipoğlu1, Mehdi Keblouti2, Babak Mohammadi3

  • 1Erzincan Binali Yıldırım University, Faculty of Engineering and Architecture, Department of Civil Engineering, Erzincan, Türkiye. okatipoglu@erzincan.edu.tr.

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Summary

Accurate streamflow estimation is vital for water resource management. This study introduces novel hybrid models combining artificial bee colony-optimized artificial neural networks with signal decomposition techniques for improved hydrological predictions.

Keywords:
Artificial bee colony optimizationEast Black Sea RegionEmpirical mode decompositionLocal mean decompositionStreamflow prediction

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Area of Science:

  • Hydrology and Water Resource Management
  • Computational Intelligence in Environmental Science

Background:

  • Accurate streamflow estimation is critical for sustainable water resource management, disaster preparedness, and various water-related applications.
  • Traditional hydrological models often face challenges in accurately predicting streamflow, particularly in regions prone to extreme events like droughts and floods.

Purpose of the Study:

  • To develop and evaluate novel hybrid models for enhanced streamflow estimation.
  • To assess the efficacy of combining Artificial Bee Colony (ABC) optimized Artificial Neural Networks (ANN) with advanced signal decomposition techniques.

Main Methods:

  • Developed an Artificial Bee Colony-Artificial Neural Network (ABC-ANN) hybrid model.
  • Integrated the ABC-ANN model with Local Mean Decomposition (LMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) signal decomposition techniques.
  • Applied these hybrid models (LMD-ABC-ANN and CEEMDAN-ABC-ANN) for streamflow prediction in the East Black Sea Region, Türkiye.

Main Results:

  • The study successfully evaluated the performance of the novel LMD-ABC-ANN and CEEMDAN-ABC-ANN hybrid approaches.
  • Demonstrated the potential of these advanced hybrid models in improving streamflow prediction accuracy.

Conclusions:

  • The developed hybrid models offer reliable strategies for enhancing streamflow estimation.
  • These findings provide valuable resources for water resource planners and policymakers in managing water resources effectively.