Outlier robust extreme learning machine to simulate discharge coefficient of side slots
Abstract As the first time, this paper attempts to recreate the discharge coefficient (DC) of side slots by another artificial intelligence procedure named "Outlier Robust Extreme Learning Machine (ORELM)". Accordingly, at first, the variables affecting the DC comprising the ratios of the...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-05-01
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| Series: | Applied Water Science |
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| Online Access: | https://doi.org/10.1007/s13201-022-01687-3 |
| _version_ | 1818213856575488000 |
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| author | Farzad Hasani Saeid Shabanlou |
| author_facet | Farzad Hasani Saeid Shabanlou |
| author_sort | Farzad Hasani |
| collection | DOAJ |
| description | Abstract As the first time, this paper attempts to recreate the discharge coefficient (DC) of side slots by another artificial intelligence procedure named "Outlier Robust Extreme Learning Machine (ORELM)". Accordingly, at first, the variables affecting the DC comprising the ratios of the flow depth to the side slot length (Y m /L), the side slot crest elevation to the side slot length (W/L), the main channel width to the side slot length (B/L), as well as the Froude number (F r) are determined and subsequently five ORELM models (ORELM 1 to ORELM 5) are created utilizing these variables. From that point forward, laboratory measurements are arranged into two datasets comprising training (70%) and testing (30%). At the subsequent stage, the best model alongside the most affecting input variables is presented by executing a sensitivity examination. The most impressive model (i.e., ORELM 3) reproduces DC values as far as B/L, W/L and F r. It is worth focusing on that ORELM 3 forecasts DC values with worthy precision. For instance, the correlation coefficient (R), the scatter index (SI) and the Nash–Sutcliffe effectiveness (NSC) for ORELM 3 are acquired in the examination state to be 0.936, 0.049 and 0.852, independently. Examining the outcomes yielded from the simulation demonstrates that W/L and F r are the most impacting factors to reproduce the DC. Besides, the findings of the sensitivity examination uncover that ORELM 3 acts in an underestimated way. Finally, a computer code is put forward to compute the DC of side slots. |
| first_indexed | 2024-12-12T06:10:56Z |
| format | Article |
| id | doaj.art-c50701d3f0274bdf878cc8636efe3b89 |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-12-12T06:10:56Z |
| publishDate | 2022-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-c50701d3f0274bdf878cc8636efe3b892022-12-22T00:35:10ZengSpringerOpenApplied Water Science2190-54872190-54952022-05-0112711410.1007/s13201-022-01687-3Outlier robust extreme learning machine to simulate discharge coefficient of side slotsFarzad Hasani0Saeid Shabanlou1Department of Water Engineering, Kermanshah Branch, Islamic Azad UniversityDepartment of Water Engineering, Kermanshah Branch, Islamic Azad UniversityAbstract As the first time, this paper attempts to recreate the discharge coefficient (DC) of side slots by another artificial intelligence procedure named "Outlier Robust Extreme Learning Machine (ORELM)". Accordingly, at first, the variables affecting the DC comprising the ratios of the flow depth to the side slot length (Y m /L), the side slot crest elevation to the side slot length (W/L), the main channel width to the side slot length (B/L), as well as the Froude number (F r) are determined and subsequently five ORELM models (ORELM 1 to ORELM 5) are created utilizing these variables. From that point forward, laboratory measurements are arranged into two datasets comprising training (70%) and testing (30%). At the subsequent stage, the best model alongside the most affecting input variables is presented by executing a sensitivity examination. The most impressive model (i.e., ORELM 3) reproduces DC values as far as B/L, W/L and F r. It is worth focusing on that ORELM 3 forecasts DC values with worthy precision. For instance, the correlation coefficient (R), the scatter index (SI) and the Nash–Sutcliffe effectiveness (NSC) for ORELM 3 are acquired in the examination state to be 0.936, 0.049 and 0.852, independently. Examining the outcomes yielded from the simulation demonstrates that W/L and F r are the most impacting factors to reproduce the DC. Besides, the findings of the sensitivity examination uncover that ORELM 3 acts in an underestimated way. Finally, a computer code is put forward to compute the DC of side slots.https://doi.org/10.1007/s13201-022-01687-3Side slotsDischarge coefficientOutlier robust extreme learning machineUncertainty analysis, Partial derivative sensitivity analysisSensitivity analysis |
| spellingShingle | Farzad Hasani Saeid Shabanlou Outlier robust extreme learning machine to simulate discharge coefficient of side slots Applied Water Science Side slots Discharge coefficient Outlier robust extreme learning machine Uncertainty analysis, Partial derivative sensitivity analysis Sensitivity analysis |
| title | Outlier robust extreme learning machine to simulate discharge coefficient of side slots |
| title_full | Outlier robust extreme learning machine to simulate discharge coefficient of side slots |
| title_fullStr | Outlier robust extreme learning machine to simulate discharge coefficient of side slots |
| title_full_unstemmed | Outlier robust extreme learning machine to simulate discharge coefficient of side slots |
| title_short | Outlier robust extreme learning machine to simulate discharge coefficient of side slots |
| title_sort | outlier robust extreme learning machine to simulate discharge coefficient of side slots |
| topic | Side slots Discharge coefficient Outlier robust extreme learning machine Uncertainty analysis, Partial derivative sensitivity analysis Sensitivity analysis |
| url | https://doi.org/10.1007/s13201-022-01687-3 |
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