Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application
The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excel...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8206245 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567429049352192 |
|---|---|
| author | Zaher Mundher Yaseen Hossam Faris Nadhir Al-Ansari |
| author_facet | Zaher Mundher Yaseen Hossam Faris Nadhir Al-Ansari |
| author_sort | Zaher Mundher Yaseen |
| collection | DOAJ |
| description | The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq. |
| format | Article |
| id | doaj-art-1e20202e952d43cdab1be2193b9c044a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1e20202e952d43cdab1be2193b9c044a2025-02-03T01:01:30ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/82062458206245Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological ApplicationZaher Mundher Yaseen0Hossam Faris1Nadhir Al-Ansari2Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, VietnamKing Abdullah II School for Information Technology, The University of Jordan, Amman, JordanCivil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, SwedenThe capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.http://dx.doi.org/10.1155/2020/8206245 |
| spellingShingle | Zaher Mundher Yaseen Hossam Faris Nadhir Al-Ansari Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application Complexity |
| title | Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application |
| title_full | Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application |
| title_fullStr | Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application |
| title_full_unstemmed | Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application |
| title_short | Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application |
| title_sort | hybridized extreme learning machine model with salp swarm algorithm a novel predictive model for hydrological application |
| url | http://dx.doi.org/10.1155/2020/8206245 |
| work_keys_str_mv | AT zahermundheryaseen hybridizedextremelearningmachinemodelwithsalpswarmalgorithmanovelpredictivemodelforhydrologicalapplication AT hossamfaris hybridizedextremelearningmachinemodelwithsalpswarmalgorithmanovelpredictivemodelforhydrologicalapplication AT nadhiralansari hybridizedextremelearningmachinemodelwithsalpswarmalgorithmanovelpredictivemodelforhydrologicalapplication |