Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Rapidly Varying Flow01:24

Rapidly Varying Flow

158
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...
158
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

137
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...
137
Plane Potential Flows01:23

Plane Potential Flows

479
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
479
Gradually Varying Flow01:29

Gradually Varying Flow

140
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...
140
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

131
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...
131
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

186
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
186

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A multi-scale CNN-GRU fusion model with stationary wavelet transform for 14-day ahead dam water level prediction.

Scientific reports·2025
Same author

Improving rainfall forecasting using deep learning data fusing model approach for observed and climate change data.

Scientific reports·2025
Same author

Overtopping risk of high-hazard embankment dam under climate change condition.

PloS one·2025
Same author

Durability and environmental evaluation of rice husk ash sustainable concrete containing carbon nanotubes.

Scientific reports·2025
Same author

Water impact analysis due to coal-electricity generation using the life cycle assessment method: a case study in Malaysia.

Water science and technology : a journal of the International Association on Water Pollution Research·2025
Same author

Metal nanoparticles entrapment in chitosan-carbon black composite hydrogel towards sustainable environmental solutions.

International journal of biological macromolecules·2025

Related Experiment Video

Updated: Sep 30, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms.

Yusuf Essam1, Yuk Feng Huang2, Jing Lin Ng3

  • 1Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malaysia.

Scientific Reports
|March 11, 2022
PubMed
Summary

Machine learning models can predict streamflow (SF) to mitigate flood and drought damage in Peninsular Malaysia. The Artificial Neural Network 3 (ANN3) model is the most effective universal tool for accurate SF prediction across various rivers.

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Related Experiment Videos

Last Updated: Sep 30, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Area of Science:

  • Environmental Science
  • Hydrology
  • Data Science

Background:

  • Floods and droughts in Peninsular Malaysia are linked to extreme streamflow (SF) variations.
  • Accurate SF prediction is crucial for mitigating environmental and municipal damages.
  • Existing prediction methods require enhancement for diverse river systems.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for streamflow prediction in Peninsular Malaysia.
  • To identify a universal ML model capable of accurately predicting SF across multiple rivers.
  • To enhance flood and drought mitigation strategies through reliable SF forecasting.

Main Methods:

  • Utilized Support Vector Machine (SVM), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) algorithms.
  • Collected and analyzed SF data from 11 rivers in Peninsular Malaysia.
  • Tested various input scenarios, including the ANN3 model with 3-day historical SF data.

Main Results:

  • The ANN3 model demonstrated superior performance in 4 out of 11 river datasets.
  • ANN3 achieved a high average Root Mean Square (RMS) score of 3.27, indicating strong predictive accuracy.
  • The ANN3 model proved adaptable and reliable across different river case studies.

Conclusions:

  • The ANN3 model, utilizing ANN with 3-day historical SF inputs, is proposed as the best universal ML model for SF prediction in Peninsular Malaysia.
  • This model offers a reliable solution for enhancing hydrological forecasting and disaster management.
  • The findings support the adoption of advanced ML techniques for environmental monitoring and mitigation efforts.