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Related Experiment Video

Updated: Mar 31, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

2.1K

Development of sediment load estimation models by using artificial neural networking techniques.

Muhammad Hassan1, M Ali Shamim2, Ali Sikandar3

  • 1Department of Civil Engineering, Mirpur University of Science & Technology, Mirpur, A.K., Pakistan. hassan25.arif@gmail.com.

Environmental Monitoring and Assessment
|October 15, 2015
PubMed
Summary
This summary is machine-generated.

This study developed an artificial neural network (ANN) model to estimate weekly sediment load in Pakistan. The BFGS algorithm provided the most accurate sediment load estimations, outperforming other models.

Keywords:
Artificial neural networksDead storageGamma testPhysical parametersSedimentation

Related Experiment Videos

Last Updated: Mar 31, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

2.1K

Area of Science:

  • Environmental science
  • Hydrology
  • Artificial intelligence

Background:

  • Sediment load estimation is crucial for water resource management.
  • Accurate prediction models are needed for catchments in regions like Northern Pakistan.

Purpose of the Study:

  • To develop and evaluate an artificial neural network (ANN) model for weekly sediment load estimation.
  • To compare the performance of different ANN training algorithms.

Main Methods:

  • Utilized antecedent sediment conditions, discharge, and temperature data.
  • Employed the Gamma test for input and data length selection.
  • Trained ANN models using Broyden-Fletcher-Goldfarb-Shanno (BFGS), back propagation (BP), and local linear regression (LLR) algorithms.

Main Results:

  • The BFGS-based ANN model demonstrated superior performance.
  • Achieved significantly low Root Mean Squared Error (RMSE) and Mean Biased Error (MBE) values.
  • High R-square value indicated a strong correlation between observed and estimated sediment load.

Conclusions:

  • The BFGS-based ANN model is effective for estimating weekly sediment load.
  • The model shows high accuracy and reliability for hydrological applications in the study region.