Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data

Gates are commonly used to adjust water flow in open channels. By using an oblique/inclined gate, the water transferring capacity of open irrigation canals can be increased. Investigation of free and submerged discharge coefficients for inclined sluice gates is the focus of the present study. First...

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Autores principales:	Farzin Salmasi, Meysam Nouri, Parveen Sihag, John Abraham
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	artificial intelligence discharge coefficient free flow inclined sluice gate submerged flow Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/4b50368146fd4c0a80f5f5414644b215
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spelling	oai:doaj.org-article:4b50368146fd4c0a80f5f5414644b2152021-11-06T07:05:40ZApplication of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data1606-97491607-079810.2166/ws.2020.226https://doaj.org/article/4b50368146fd4c0a80f5f5414644b2152021-02-01T00:00:00Zhttp://ws.iwaponline.com/content/21/1/232https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Gates are commonly used to adjust water flow in open channels. By using an oblique/inclined gate, the water transferring capacity of open irrigation canals can be increased. Investigation of free and submerged discharge coefficients for inclined sluice gates is the focus of the present study. First an experimental apparatus incorporating an inclined gate was created. The inclined angle (β) and gate opening (a) were experiment variables, and the five inclination angles include: 0° (vertical gate), 15°, 30°, 45° and 60°. Experimental results showed a greater convergence of flow lines under the gate and increasing the gate angle causes the discharge coefficient to increase. Also experiments showed that increasing the submergence rate (yt/a), decreases the inclined gate discharge coefficient. Performance metrics were created for the experimental results. The metrics utilized Gaussian process (GP) regression, support vector machine (SVM), artificial neural networks (ANN), generalized regression neural network (GRNN), random forest (RF) regression and random tree (RT) based models which were used to predict discharge coefficients (Cd) in both submerged and free flow conditions. The model input parameters were the ratio of the upstream water depth to gate opening (y/a) and the inclined angle (β) for free flow and also the submergence rate (yt/a) for submerged flow. The prediction models show that the ANN model in free flow conditions has the following performance metrics: Coefficient of determination, R2 = 0.9957, Root Mean Square Error (RMSE) = 0.0044, and Mean Absolute Error (MAE) = 0.0017. The performance metrics for submerged flow conditions were R2 = 0.9922, RMSE = 0.0079 and MAE = 0.0054. The ANN approach is the most accurate model compared to the others.Farzin SalmasiMeysam NouriParveen SihagJohn AbrahamIWA Publishingarticleartificial intelligencedischarge coefficientfree flowinclined sluice gatesubmerged flowWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 1, Pp 232-248 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence discharge coefficient free flow inclined sluice gate submerged flow Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	artificial intelligence discharge coefficient free flow inclined sluice gate submerged flow Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Farzin Salmasi Meysam Nouri Parveen Sihag John Abraham Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data
description	Gates are commonly used to adjust water flow in open channels. By using an oblique/inclined gate, the water transferring capacity of open irrigation canals can be increased. Investigation of free and submerged discharge coefficients for inclined sluice gates is the focus of the present study. First an experimental apparatus incorporating an inclined gate was created. The inclined angle (β) and gate opening (a) were experiment variables, and the five inclination angles include: 0° (vertical gate), 15°, 30°, 45° and 60°. Experimental results showed a greater convergence of flow lines under the gate and increasing the gate angle causes the discharge coefficient to increase. Also experiments showed that increasing the submergence rate (yt/a), decreases the inclined gate discharge coefficient. Performance metrics were created for the experimental results. The metrics utilized Gaussian process (GP) regression, support vector machine (SVM), artificial neural networks (ANN), generalized regression neural network (GRNN), random forest (RF) regression and random tree (RT) based models which were used to predict discharge coefficients (Cd) in both submerged and free flow conditions. The model input parameters were the ratio of the upstream water depth to gate opening (y/a) and the inclined angle (β) for free flow and also the submergence rate (yt/a) for submerged flow. The prediction models show that the ANN model in free flow conditions has the following performance metrics: Coefficient of determination, R2 = 0.9957, Root Mean Square Error (RMSE) = 0.0044, and Mean Absolute Error (MAE) = 0.0017. The performance metrics for submerged flow conditions were R2 = 0.9922, RMSE = 0.0079 and MAE = 0.0054. The ANN approach is the most accurate model compared to the others.
format	article
author	Farzin Salmasi Meysam Nouri Parveen Sihag John Abraham
author_facet	Farzin Salmasi Meysam Nouri Parveen Sihag John Abraham
author_sort	Farzin Salmasi
title	Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data
title_short	Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data
title_full	Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data
title_fullStr	Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data
title_full_unstemmed	Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data
title_sort	application of svm, ann, grnn, rf, gp and rt models for predicting discharge coefficients of oblique sluice gates using experimental data
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/4b50368146fd4c0a80f5f5414644b215
work_keys_str_mv	AT farzinsalmasi applicationofsvmanngrnnrfgpandrtmodelsforpredictingdischargecoefficientsofobliquesluicegatesusingexperimentaldata AT meysamnouri applicationofsvmanngrnnrfgpandrtmodelsforpredictingdischargecoefficientsofobliquesluicegatesusingexperimentaldata AT parveensihag applicationofsvmanngrnnrfgpandrtmodelsforpredictingdischargecoefficientsofobliquesluicegatesusingexperimentaldata AT johnabraham applicationofsvmanngrnnrfgpandrtmodelsforpredictingdischargecoefficientsofobliquesluicegatesusingexperimentaldata
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Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data

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