Prediction of seepage flow through earthfill dams using machine learning models
In this study, three machine learning models, namely, the Multilayer Perceptron Neural Networks (MLPNN), the Generalized Regression Neural Networks (GRNN) and the Radial Basis Function Neural Networks (RBFNN) were used for predicting seepage flow through an earthfill dam. Moreover, obtained results...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | HydroResearch |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589757824000052 |
| _version_ | 1797334747922300928 |
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| author | Issam Rehamnia Ahmed Mohammed Sami Al-Janabi Saad Sh. Sammen Binh Thai Pham Indra Prakash |
| author_facet | Issam Rehamnia Ahmed Mohammed Sami Al-Janabi Saad Sh. Sammen Binh Thai Pham Indra Prakash |
| author_sort | Issam Rehamnia |
| collection | DOAJ |
| description | In this study, three machine learning models, namely, the Multilayer Perceptron Neural Networks (MLPNN), the Generalized Regression Neural Networks (GRNN) and the Radial Basis Function Neural Networks (RBFNN) were used for predicting seepage flow through an earthfill dam. Moreover, obtained results were compared with those obtained from the standard Multiple Linear Regression (MLR). The three models were developed using piezometer elevations observed at seven different piezometers, in addition to the related reservoir water level and the periodicity for a period of seven years. Obtained results indicated that the GRNN model had substantially better prediction performance than the RBFNN, MLPNN, and the standard MLR models with statistical values of coefficient of correlation R = 0.981, root mean square error RMSE = 0.386 L/s, and a mean absolute error MAE = 0.95 L/s. Moreover, including the periodicity factors improves prediction accuracy of the machine learning models. |
| first_indexed | 2024-03-08T08:26:38Z |
| format | Article |
| id | doaj.art-9b4e0988614748a1aba071e84738a599 |
| institution | Directory Open Access Journal |
| issn | 2589-7578 |
| language | English |
| last_indexed | 2024-03-08T08:26:38Z |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | HydroResearch |
| spelling | doaj.art-9b4e0988614748a1aba071e84738a5992024-02-02T04:40:06ZengKeAi Communications Co., Ltd.HydroResearch2589-75782024-01-017131139Prediction of seepage flow through earthfill dams using machine learning modelsIssam Rehamnia0Ahmed Mohammed Sami Al-Janabi1Saad Sh. Sammen2Binh Thai Pham3Indra Prakash4Laboratory of Water Resources Mobilization and Valorization (MVRE), National High School for Hydraulic (ENSH), Blida BP 31, AlgeriaDepartment of Civil Engineering, Cihan University-Erbil, Kurdistan Region, Iraq; Corresponding author at: Department of Civil Engineering, Cihan University-Erbil, Kurdistan Region, Iraq.Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, IraqUniversity of Transport Technology, Hanoi, Viet NamDDG(R) Geological Survey of India, Gandhinagar 382010, IndiaIn this study, three machine learning models, namely, the Multilayer Perceptron Neural Networks (MLPNN), the Generalized Regression Neural Networks (GRNN) and the Radial Basis Function Neural Networks (RBFNN) were used for predicting seepage flow through an earthfill dam. Moreover, obtained results were compared with those obtained from the standard Multiple Linear Regression (MLR). The three models were developed using piezometer elevations observed at seven different piezometers, in addition to the related reservoir water level and the periodicity for a period of seven years. Obtained results indicated that the GRNN model had substantially better prediction performance than the RBFNN, MLPNN, and the standard MLR models with statistical values of coefficient of correlation R = 0.981, root mean square error RMSE = 0.386 L/s, and a mean absolute error MAE = 0.95 L/s. Moreover, including the periodicity factors improves prediction accuracy of the machine learning models.http://www.sciencedirect.com/science/article/pii/S2589757824000052PredictionSeepage flowEarthfill damsMachine learningFontaine gazelles dam |
| spellingShingle | Issam Rehamnia Ahmed Mohammed Sami Al-Janabi Saad Sh. Sammen Binh Thai Pham Indra Prakash Prediction of seepage flow through earthfill dams using machine learning models HydroResearch Prediction Seepage flow Earthfill dams Machine learning Fontaine gazelles dam |
| title | Prediction of seepage flow through earthfill dams using machine learning models |
| title_full | Prediction of seepage flow through earthfill dams using machine learning models |
| title_fullStr | Prediction of seepage flow through earthfill dams using machine learning models |
| title_full_unstemmed | Prediction of seepage flow through earthfill dams using machine learning models |
| title_short | Prediction of seepage flow through earthfill dams using machine learning models |
| title_sort | prediction of seepage flow through earthfill dams using machine learning models |
| topic | Prediction Seepage flow Earthfill dams Machine learning Fontaine gazelles dam |
| url | http://www.sciencedirect.com/science/article/pii/S2589757824000052 |
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