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Related Concept Videos

Rapidly Varying Flow01:24

Rapidly Varying Flow

177
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...
177
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

738
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
738
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

162
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...
162
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

206
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...
206
Gradually Varying Flow01:29

Gradually Varying Flow

161
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...
161
Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Related Experiment Videos

Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term

Sujan Ghimire1, Zaher Mundher Yaseen2,3, Aitazaz A Farooque4

  • 1School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.

Scientific Reports
|September 2, 2021
PubMed
Summary

A new CNN-LSTM model accurately predicts streamflow (Qflow) for water management. This integrated approach outperforms existing AI models for short-term flow forecasting, showing significant practical value.

Related Experiment Videos

Area of Science:

  • Hydrology and Water Resources Engineering
  • Artificial Intelligence in Environmental Science
  • Time Series Analysis and Forecasting

Background:

  • Accurate streamflow (Qflow) prediction is crucial for water resource management, impacting hydropower, agriculture, and flood control.
  • Existing artificial intelligence (AI) models face challenges in capturing complex temporal dynamics for precise streamflow forecasting.

Purpose of the Study:

  • To develop and evaluate a novel integrated Convolutional Neural Network (CNN) and Long-Short-term Memory (LSTM) network model (CNN-LSTM) for short-term streamflow prediction.
  • To benchmark the performance of the proposed CNN-LSTM model against standalone CNN, LSTM, Deep Neural Network (DNN), and other conventional AI models.
  • To assess the predictive accuracy of the CNN-LSTM model across various forecasting horizons, including 1-week, 2-weeks, 4-weeks, and 9-months.

Main Methods:

  • Utilized CNN layers for feature extraction from streamflow time-series data.
  • Employed LSTM networks to leverage extracted features for streamflow time-series prediction.
  • Integrated CNN and LSTM into a hybrid CNN-LSTM model for enhanced predictive capabilities.
  • Performed comparative analysis using performance metrics and graphical visualizations against benchmark models.

Main Results:

  • The CNN-LSTM model demonstrated superior performance in streamflow prediction compared to standalone CNN, LSTM, DNN, and other AI models across all tested time intervals.
  • The proposed model achieved a small residual error between actual and predicted streamflow values.
  • Specifically, 84% of streamflow prediction errors for CNN-LSTM were below 0.05 m³s⁻¹, outperforming LSTM (80%) and DNN (66%).

Conclusions:

  • The novel CNN-LSTM framework provides more accurate streamflow predictions than existing methods.
  • The enhanced predictive accuracy of CNN-LSTM highlights its significant practical value for robust water resource planning and management.
  • The model's ability to outperform benchmarks across diverse time scales underscores its potential for real-world hydrological applications.