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Updated: Jun 3, 2026
10:12
Analyzing Mixing Inhomogeneity in a Microfluidic Device by Microscale Schlieren Technique
Published on: June 12, 2015
ANFIS-based approach for the estimation of transverse mixing coefficient
Z Ahmad1, H Md Azamathulla, N A Zakaria
1Associate Professor, Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee-247667, (Uttarakhand), India. zulfifce@iitr.ernet.in
Summary
This study introduces an Adaptive Neuro Fuzzy Inference System (ANFIS) to predict pollutant transverse mixing coefficients in open channels. The ANFIS model accurately estimates mixing, outperforming existing methods.
Area of Science:
- Environmental Engineering
- Fluid Mechanics
- Water Quality Management
Background:
- Effective pollution control requires understanding pollutant fate in streams.
- Transverse mixing is crucial for pollutant dispersion in open channels.
- Previous research linked transverse mixing coefficients to channel and flow parameters.
Purpose of the Study:
- To develop and present an Adaptive Neuro Fuzzy Inference System (ANFIS) for predicting transverse mixing coefficients.
- To assess the performance of ANFIS against Artificial Neural Network (ANN) models and existing predictors.
- To utilize a comprehensive dataset of laboratory and field data for model validation.
Main Methods:
- Implementation of the Adaptive Neuro Fuzzy Inference System (ANFIS) approach.
- Utilizing a diverse dataset encompassing various channel and flow conditions.
- Comparative analysis with Artificial Neural Network (ANN) models and established prediction methods.
Main Results:
- The ANFIS approach demonstrated high accuracy in predicting transverse mixing coefficients.
- Achieved a coefficient of determination (R²) of 0.945.
- The proposed ANFIS method showed superior performance compared to ANN and existing predictors.
Conclusions:
- ANFIS is a highly effective tool for predicting transverse mixing coefficients in open channel flows.
- The developed model provides reliable predictions across a wide range of conditions.
- This approach offers a significant advancement in water pollution control strategies.
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