Estimation of discharge correction factor
of modified Parshall flume using ANFIS
and ANN
Authors: Saran D. , Tiwari N.K. 
Published: November 2020

Purpose: To evaluate and compare the capability of ANFIS (Adaptive Neuro-Fuzzy-Inference
System), ANN (Artificial Neural Network), and MNLR (Multiple Non-linear Regression)
techniques in the estimation and formulation of Discharge Correction Factor (Cd) of modified
Parshall flumes as based on linear relations and errors between input and output data.
Design/methodology/approach: Acknowledging the necessity of further research in
this field, experiments were conducted in the Hydraulics Laboratory of Civil Engineering
Department, National Institute of Technology, Kurukshetra, India. The Parshall flume
characteristics, associated longitudinal slopes and the discharge passing through the flume
were varied. Consequent water depths at specific points in Parshall flumes were noted
and the values of Cd were computed. In this manner, a data set of 128 observations was
acquired. This was bifurcated arbitrarily into a training dataset consisting of 88 observations
and a testing dataset consisting of 40 observations. Models developed using the training
dataset were checked on the testing dataset for comparison of the performance of each
predictive model. Further, an empirical relationship was formulated establishing Cd as a
function of flume characteristics, longitudinal slope, and water depth at specific points using
the MNLR technique. Moreover, Cd was estimated using soft computing tools; ANFIS and
ANN. Finally, a sensitivity analysis was done to find out the flume variable having the greatest
influence on the estimation of Cd.
Findings: The predictive accuracy of the ANN-based model was found to be better than
the model developed using ANFIS, followed by the model developed using the MNLR
technique. Further, sensitivity analysis results indicated that primary depth reading (Ha) as
input parameter has the greatest influence on the prediction capability of the developed
model.
Research limitations/implications: Since the soft computing models are data based
learning, hence the prediction capability of these models may dwindle if data is selected
beyond the current data range, which is based on the experiments conducted under specific
conditions. Further, since the present study has faced time and facility constraints, hence
there is still a huge scope of research in this field. Different lateral slopes, combined laterallongitudinal
slopes, and more modified Parshall flume models of larger sizes can be added
to increase the versatility of the current research.
Practical implications: Cd of modified Parshall flumes can be predicted using the ANNbased
prediction model more accurately as compared to other considered techniques.
Originality/value: The comparative analysis of prediction models, as well as the
formulation of relation, has been conducted in this study. In all the previous works, little to
no soft computing techniques have been applied for the analysis of Parshall flumes. Even
the regression techniques have been applied only on Parshall flumes of standard sizes.
However, this paper includes not only Parshall flume of standard size but also a modified
Parshall flume in its pursuit of predicting Cd with the help of ANN and ANFIS based
prediction models along with MNLR technique