Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm
Accurate monthly runoff prediction is still challenging work regardless of the accessibility of different modelling techniques, like the knowledge-driven or data-driven models, and human activities and climate changes. To this context, applicability of hybrid SVM-SSA (Support Vector Machine with Sal...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447922000430 |
| _version_ | 1811250610709725184 |
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| author | Sandeep Samantaray Sambit Sawan Das Abinash Sahoo Deba Prakash Satapathy |
| author_facet | Sandeep Samantaray Sambit Sawan Das Abinash Sahoo Deba Prakash Satapathy |
| author_sort | Sandeep Samantaray |
| collection | DOAJ |
| description | Accurate monthly runoff prediction is still challenging work regardless of the accessibility of different modelling techniques, like the knowledge-driven or data-driven models, and human activities and climate changes. To this context, applicability of hybrid SVM-SSA (Support Vector Machine with Salp Swarm Algorithm) model and conventional SVM and artificial neural network (ANN) models is investigated for runoff prediction in Baitarani river basin, Odisha, India. Potential of proposed techniques is measured utilising four quantitative indexes, root mean squared error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and Willmott index (WI). Test results specify that hybrid model generates better prediction accurateness in comparison to applied conventional methods. The generalization and robustness of SVM-SSA techniques were very prominent, with R2 values of 0.9847 and 0.9844 for Anandpur and Champua stations during training phases. Similarly prominent value of WI are 0.9906, 0.9902 and minimum value of RSE and MAE are 20.019, 0.5928 and 0.0769, 0.5934 for Anandpur and Champua stations respectively. Therefore, SVM-SSA can be recommended for modeling complexity of interactions for rainfall-runoff process and predicting runoff. |
| first_indexed | 2024-04-12T16:07:09Z |
| format | Article |
| id | doaj.art-5f3773eba6714c9f99926b9d38f91db2 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-04-12T16:07:09Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-5f3773eba6714c9f99926b9d38f91db22022-12-22T03:26:01ZengElsevierAin Shams Engineering Journal2090-44792022-09-01135101732Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithmSandeep Samantaray0Sambit Sawan Das1Abinash Sahoo2Deba Prakash Satapathy3Department of Civil Engineering, College of Engineering and Technology, Bhubaneswar, Odisha 751003, India; Department of Civil Engineering, National Institute of Technology, Silchar, Assam 788010, India; Corresponding author at: Department of Civil Engineering, National Institute of Technology, Silchar, Assam 788010, India.Department of Civil Engineering, College of Engineering and Technology, Bhubaneswar, Odisha 751003, IndiaDepartment of Civil Engineering, National Institute of Technology, Silchar, Assam 788010, IndiaDepartment of Civil Engineering, College of Engineering and Technology, Bhubaneswar, Odisha 751003, IndiaAccurate monthly runoff prediction is still challenging work regardless of the accessibility of different modelling techniques, like the knowledge-driven or data-driven models, and human activities and climate changes. To this context, applicability of hybrid SVM-SSA (Support Vector Machine with Salp Swarm Algorithm) model and conventional SVM and artificial neural network (ANN) models is investigated for runoff prediction in Baitarani river basin, Odisha, India. Potential of proposed techniques is measured utilising four quantitative indexes, root mean squared error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and Willmott index (WI). Test results specify that hybrid model generates better prediction accurateness in comparison to applied conventional methods. The generalization and robustness of SVM-SSA techniques were very prominent, with R2 values of 0.9847 and 0.9844 for Anandpur and Champua stations during training phases. Similarly prominent value of WI are 0.9906, 0.9902 and minimum value of RSE and MAE are 20.019, 0.5928 and 0.0769, 0.5934 for Anandpur and Champua stations respectively. Therefore, SVM-SSA can be recommended for modeling complexity of interactions for rainfall-runoff process and predicting runoff.http://www.sciencedirect.com/science/article/pii/S2090447922000430Baitarani RiverRunoffSupport vector machineSalp Swarm Algorithm |
| spellingShingle | Sandeep Samantaray Sambit Sawan Das Abinash Sahoo Deba Prakash Satapathy Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm Ain Shams Engineering Journal Baitarani River Runoff Support vector machine Salp Swarm Algorithm |
| title | Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm |
| title_full | Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm |
| title_fullStr | Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm |
| title_full_unstemmed | Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm |
| title_short | Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm |
| title_sort | monthly runoff prediction at baitarani river basin by support vector machine based on salp swarm algorithm |
| topic | Baitarani River Runoff Support vector machine Salp Swarm Algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2090447922000430 |
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