Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models

Inflatable dams are flexible hydraulic structures that are constructed on rivers and are inflated by fluids such as air or water. This research investigates the effects of influential dimensionless factors on estimating one of the critical hydraulic characteristics of inflatable dams, namely the dis...

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Autores principales:	Wenyu Zheng, Shahab S. Band, Hojat Karami, Sohrab Karimi, Saeed Samadianfard, Sadra Shadkani, Kwok-Wing Chau, Amir H. Mosavi
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	inflatable dams particle swarm optimization genetic algorithm machine learning artificial intelligence Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/934acee735a744819036db2651b589cd
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spelling	oai:doaj.org-article:934acee735a744819036db2651b589cd2021-11-04T15:00:43ZForecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models1994-20601997-003X10.1080/19942060.2021.1976280https://doaj.org/article/934acee735a744819036db2651b589cd2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/19942060.2021.1976280https://doaj.org/toc/1994-2060https://doaj.org/toc/1997-003XInflatable dams are flexible hydraulic structures that are constructed on rivers and are inflated by fluids such as air or water. This research investigates the effects of influential dimensionless factors on estimating one of the critical hydraulic characteristics of inflatable dams, namely the discharge capacity. Various parameters such as the proportion of total upstream head to dam height (H1/Dh), the ratio of overflowing head to dam height (h/Dh), the ratio of discharge per unit width to its maximum value (q/qmax), the ratio of the internal pressure of the tube to its maximum value (p/pmax) and the ratio of the longitudinal coordinate placement of each element to xmax are used. A hybrid model based on the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA), PSO-GA, is proposed to improve the accuracy of the estimation by combining the advantages of both algorithms. Moreover, the performance of the model is compared with available hybrid models, including the Artificial Neural Networks (ANNs) optimized by Stochastic Gradient Descent (SGD) model (ANN-SGD) and the ANN-PSO and ANN-GA models. Finally, the performance of the algorithms is evaluated using statistical indicators such as the coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE) and the scatter index (SI). The results show that the internal pressure plays a vital role with respect to forecasting the discharge coefficient, and omitting it degrades the accuracy by 2.12%. In comparison with other models, the proposed PSO-GA hybrid model provides the most accurate results (R2 = 0.999, MAPE = 0.04). Finally, comparing the results of the proposed PSO-GA with the benchmarked ANN-GA, ANN-PSO and ANN-SGD methods proves the superiority of the hybrid PSO-GA method.Wenyu ZhengShahab S. BandHojat KaramiSohrab KarimiSaeed SamadianfardSadra ShadkaniKwok-Wing ChauAmir H. MosaviTaylor & Francis Grouparticleinflatable damsparticle swarm optimizationgenetic algorithmmachine learningartificial intelligenceEngineering (General). Civil engineering (General)TA1-2040ENEngineering Applications of Computational Fluid Mechanics, Vol 15, Iss 1, Pp 1761-1774 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	inflatable dams particle swarm optimization genetic algorithm machine learning artificial intelligence Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	inflatable dams particle swarm optimization genetic algorithm machine learning artificial intelligence Engineering (General). Civil engineering (General) TA1-2040 Wenyu Zheng Shahab S. Band Hojat Karami Sohrab Karimi Saeed Samadianfard Sadra Shadkani Kwok-Wing Chau Amir H. Mosavi Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models
description	Inflatable dams are flexible hydraulic structures that are constructed on rivers and are inflated by fluids such as air or water. This research investigates the effects of influential dimensionless factors on estimating one of the critical hydraulic characteristics of inflatable dams, namely the discharge capacity. Various parameters such as the proportion of total upstream head to dam height (H1/Dh), the ratio of overflowing head to dam height (h/Dh), the ratio of discharge per unit width to its maximum value (q/qmax), the ratio of the internal pressure of the tube to its maximum value (p/pmax) and the ratio of the longitudinal coordinate placement of each element to xmax are used. A hybrid model based on the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA), PSO-GA, is proposed to improve the accuracy of the estimation by combining the advantages of both algorithms. Moreover, the performance of the model is compared with available hybrid models, including the Artificial Neural Networks (ANNs) optimized by Stochastic Gradient Descent (SGD) model (ANN-SGD) and the ANN-PSO and ANN-GA models. Finally, the performance of the algorithms is evaluated using statistical indicators such as the coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE) and the scatter index (SI). The results show that the internal pressure plays a vital role with respect to forecasting the discharge coefficient, and omitting it degrades the accuracy by 2.12%. In comparison with other models, the proposed PSO-GA hybrid model provides the most accurate results (R2 = 0.999, MAPE = 0.04). Finally, comparing the results of the proposed PSO-GA with the benchmarked ANN-GA, ANN-PSO and ANN-SGD methods proves the superiority of the hybrid PSO-GA method.
format	article
author	Wenyu Zheng Shahab S. Band Hojat Karami Sohrab Karimi Saeed Samadianfard Sadra Shadkani Kwok-Wing Chau Amir H. Mosavi
author_facet	Wenyu Zheng Shahab S. Band Hojat Karami Sohrab Karimi Saeed Samadianfard Sadra Shadkani Kwok-Wing Chau Amir H. Mosavi
author_sort	Wenyu Zheng
title	Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models
title_short	Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models
title_full	Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models
title_fullStr	Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models
title_full_unstemmed	Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models
title_sort	forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/934acee735a744819036db2651b589cd
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Forecasting the discharge capacity of inflatable rubber dams using hybrid machine learning models

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