Modeling of the oxygen aeration performance efficiency of gabion spillways
The current paper discussed the application and comparison of machine learning algorithms such as the gradient boosting machine (GBM), neural network (NN), and deep neural network (DNN) in estimating the oxygen aeration performance efficiency (OAPE20) of the gabion spillways. Besides, traditional eq...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-11-01
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| Series: | Water Practice and Technology |
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| Online Access: | http://wpt.iwaponline.com/content/17/11/2317 |
| _version_ | 1797903765822504960 |
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| author | Rathod Srinivas N. K. Tiwari |
| author_facet | Rathod Srinivas N. K. Tiwari |
| author_sort | Rathod Srinivas |
| collection | DOAJ |
| description | The current paper discussed the application and comparison of machine learning algorithms such as the gradient boosting machine (GBM), neural network (NN), and deep neural network (DNN) in estimating the oxygen aeration performance efficiency (OAPE20) of the gabion spillways. Besides, traditional equations, namely developed multivariable linear regression (MLR) and multivariable nonlinear regression (MNLR) along with the previous models were also employed in estimating OAPE20 of the gabion spillways. Results in the testing phase showed that the DNN with the highest value of correlation (correlation of coefficient (CC) = 0.9713) and lowest values of errors (root mean square error (RMSE) = 0.1684, mean squared error (MSE) = 0.0283, and mean absolute error (MAE) = 0.1532) demonstrated the best results in estimating OAPE20 of the gabion spillways; however, other applied models such as GBM, NN, MLR, and MNLR were giving comparable results evaluated to statistical appraisal metrics, but previous studies were performing incredibly poor with the lowest value of correlation and highest values of errors. The datasets employed here were collected by conducting experiments. From the relative significance of input parameters, the Reynolds number (Re) was observed to be a crucial parameter. At the same time, the ratio of the mean size gabion materials to the length of the gabion spillway (d50/L) had the least impact over the OAPE20 of the gabion spillways.
HIGHLIGHTS
The test for the aeration performance efficiency of gabion spillways was studied.;
Machine learning techniques were used for estimating the gabion spillway aeration efficiency.;
The estimating potential of DNN, GBM, NN, etc., was compared.;
The DNN model outperformed the other proposed models.;
A sensitivity test was conducted to know the relative impact of the input variable on the output results.; |
| first_indexed | 2024-04-10T09:37:59Z |
| format | Article |
| id | doaj.art-c79dda8c6db14720b16c4cf204782b84 |
| institution | Directory Open Access Journal |
| issn | 1751-231X |
| language | English |
| last_indexed | 2024-04-10T09:37:59Z |
| publishDate | 2022-11-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Practice and Technology |
| spelling | doaj.art-c79dda8c6db14720b16c4cf204782b842023-02-17T17:25:45ZengIWA PublishingWater Practice and Technology1751-231X2022-11-0117112317233310.2166/wpt.2022.139139Modeling of the oxygen aeration performance efficiency of gabion spillwaysRathod Srinivas0N. K. Tiwari1 Department of Civil Engineering, NIT, Kurukshetra 136 119, India Department of Civil Engineering, NIT, Kurukshetra 136 119, India The current paper discussed the application and comparison of machine learning algorithms such as the gradient boosting machine (GBM), neural network (NN), and deep neural network (DNN) in estimating the oxygen aeration performance efficiency (OAPE20) of the gabion spillways. Besides, traditional equations, namely developed multivariable linear regression (MLR) and multivariable nonlinear regression (MNLR) along with the previous models were also employed in estimating OAPE20 of the gabion spillways. Results in the testing phase showed that the DNN with the highest value of correlation (correlation of coefficient (CC) = 0.9713) and lowest values of errors (root mean square error (RMSE) = 0.1684, mean squared error (MSE) = 0.0283, and mean absolute error (MAE) = 0.1532) demonstrated the best results in estimating OAPE20 of the gabion spillways; however, other applied models such as GBM, NN, MLR, and MNLR were giving comparable results evaluated to statistical appraisal metrics, but previous studies were performing incredibly poor with the lowest value of correlation and highest values of errors. The datasets employed here were collected by conducting experiments. From the relative significance of input parameters, the Reynolds number (Re) was observed to be a crucial parameter. At the same time, the ratio of the mean size gabion materials to the length of the gabion spillway (d50/L) had the least impact over the OAPE20 of the gabion spillways. HIGHLIGHTS The test for the aeration performance efficiency of gabion spillways was studied.; Machine learning techniques were used for estimating the gabion spillway aeration efficiency.; The estimating potential of DNN, GBM, NN, etc., was compared.; The DNN model outperformed the other proposed models.; A sensitivity test was conducted to know the relative impact of the input variable on the output results.;http://wpt.iwaponline.com/content/17/11/2317deep neural network (dnn)gradient boosting machine (gbm)neural network (nn)oxygen aeration performance efficiency (oape20) of the gabion spillwayreynolds number (re)porosity (n) |
| spellingShingle | Rathod Srinivas N. K. Tiwari Modeling of the oxygen aeration performance efficiency of gabion spillways Water Practice and Technology deep neural network (dnn) gradient boosting machine (gbm) neural network (nn) oxygen aeration performance efficiency (oape20) of the gabion spillway reynolds number (re) porosity (n) |
| title | Modeling of the oxygen aeration performance efficiency of gabion spillways |
| title_full | Modeling of the oxygen aeration performance efficiency of gabion spillways |
| title_fullStr | Modeling of the oxygen aeration performance efficiency of gabion spillways |
| title_full_unstemmed | Modeling of the oxygen aeration performance efficiency of gabion spillways |
| title_short | Modeling of the oxygen aeration performance efficiency of gabion spillways |
| title_sort | modeling of the oxygen aeration performance efficiency of gabion spillways |
| topic | deep neural network (dnn) gradient boosting machine (gbm) neural network (nn) oxygen aeration performance efficiency (oape20) of the gabion spillway reynolds number (re) porosity (n) |
| url | http://wpt.iwaponline.com/content/17/11/2317 |
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