Prediction of the discharge capacity of piano key weirs using artificial neural networks

ABSTRACT The discharge capacity of the piano key weir (PKW) is an important flow feature which ultimately decides the design of PKWs. In the present research work, the different architecture of artificial neural networks (ANNs) was employed to predict the discharge capacity of the trapezoidal piano...

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Bibliographic Details
Published inJournal of hydroinformatics Vol. 26; no. 5; pp. 1167 - 1188
Main Authors Iqbal, Mujahid, Ghani, Usman
Format Journal Article
LanguageEnglish
Published London IWA Publishing 01.05.2024
Subjects
Accuracy
Algorithms
Artificial intelligence
artificial neural network
Artificial neural networks
Canals
coefficient of discharge
Construction
Discharge
Discharge capacity
Efficiency
Fuzzy logic
Hydraulics
Neural networks
Pandemics
Parameters
Performance evaluation
Piano
Pianos
Regression analysis
Sensitivity analysis
soft computing techniques
Statistical analysis
Statistical models
Stream flow
Support vector machines
trapezoidal piano key weir
Weirs
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Summary:ABSTRACT The discharge capacity of the piano key weir (PKW) is an important flow feature which ultimately decides the design of PKWs. In the present research work, the different architecture of artificial neural networks (ANNs) was employed to predict the discharge capacity of the trapezoidal piano key weir (TPKW) by varying geometric parameters. Furthermore, adaptive neuro-fuzzy interference system (ANFIS), support vector machines (SVMs) and non-linear regression (RM) techniques were also applied to compare the performance of best ANN models. The performance of each model was evaluated using statistical indices including scatter-index (SI); coefficient of determination (R2) and mean square error (MSE). The prediction capability of all the models was found to be satisfactory. However, results predicted by ANN-22(H-15) [R2 = 0.998, MSE= 0.0024, SI = 0.0177] were more accurate than ANFIS (R2 = 0.995, MSE = 0.00039, SI=0.0256), SVM (R2 = 0.982, MSE = 0.000864, SI =0.0395) and RM (R2 = 0.978, MSE = 0.001, SI = 0.0411). It was observed that Si/So, Wi/Wo and L/W ratios have the greatest effect on the discharge performance of TPKW. Furthermore, sensitivity analysis confirmed that h/P is the most influencing ratio which may considerably affect the discharge efficiency of the TPKW and ANN architecture having a single hidden layer and keeping neurons three times of input parameters in hidden layers generated better results.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2024.303