Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region
The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow of Pahang River, located in a tropical climatic region of Peninsular Malaysia. Three different bio-inspired optimization algorithms namely particle swarm...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2019
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| Online Access: | http://eprints.utm.my/89503/1/ZaherMundherYaseen2019_ImplementationofUnivariateParadigmforStreamflowSimulation.pdf |
| _version_ | 1796864843261673472 |
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| author | Yaseen, Zaher Mundher Wan Mohtar, Wan Hanna Melini Ameen, Ameen Mohammed Salih Ebtehaj, Isa Mohd. Razali, Siti Fatin Bonakdari, Hossein Salih, Sinan Q. Al-Ansari, Nadhir Shahid, Shamsuddin |
| author_facet | Yaseen, Zaher Mundher Wan Mohtar, Wan Hanna Melini Ameen, Ameen Mohammed Salih Ebtehaj, Isa Mohd. Razali, Siti Fatin Bonakdari, Hossein Salih, Sinan Q. Al-Ansari, Nadhir Shahid, Shamsuddin |
| author_sort | Yaseen, Zaher Mundher |
| collection | ePrints |
| description | The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow of Pahang River, located in a tropical climatic region of Peninsular Malaysia. Three different bio-inspired optimization algorithms namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE) were individually used to tune the membership function of ANFIS model in order to improve the capability of streamflow forecasting. Different combination of antecedent streamflow was used to develop the forecasting models. The performance of the models was evaluated using a number of metrics including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Willmott's Index (WI) statistics. The results revealed that increasing number of inputs has a positive impact on the forecasting ability of both ANFIS and hybrid ANFIS models. The comparison of the performance of three optimization methods indicated PSO improved the capability of ANFIS model (RMSE = 7.96; MAE = 2.34; R2=0.998 and WI = 0.994) more compared to GA and DE in forecasting streamflow. The uncertainty band of ANFIS-PSO forecast was also found the lowest (±0.217), which indicates that ANFIS-PSO model can be used for reliable forecasting of highly stochastic river flow in tropical environment. |
| first_indexed | 2024-03-05T20:47:54Z |
| format | Article |
| id | utm.eprints-89503 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:47:54Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | utm.eprints-895032021-02-22T06:08:03Z http://eprints.utm.my/89503/ Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region Yaseen, Zaher Mundher Wan Mohtar, Wan Hanna Melini Ameen, Ameen Mohammed Salih Ebtehaj, Isa Mohd. Razali, Siti Fatin Bonakdari, Hossein Salih, Sinan Q. Al-Ansari, Nadhir Shahid, Shamsuddin TA Engineering (General). Civil engineering (General) The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow of Pahang River, located in a tropical climatic region of Peninsular Malaysia. Three different bio-inspired optimization algorithms namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE) were individually used to tune the membership function of ANFIS model in order to improve the capability of streamflow forecasting. Different combination of antecedent streamflow was used to develop the forecasting models. The performance of the models was evaluated using a number of metrics including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Willmott's Index (WI) statistics. The results revealed that increasing number of inputs has a positive impact on the forecasting ability of both ANFIS and hybrid ANFIS models. The comparison of the performance of three optimization methods indicated PSO improved the capability of ANFIS model (RMSE = 7.96; MAE = 2.34; R2=0.998 and WI = 0.994) more compared to GA and DE in forecasting streamflow. The uncertainty band of ANFIS-PSO forecast was also found the lowest (±0.217), which indicates that ANFIS-PSO model can be used for reliable forecasting of highly stochastic river flow in tropical environment. Institute of Electrical and Electronics Engineers Inc. 2019-06 Article PeerReviewed application/pdf en http://eprints.utm.my/89503/1/ZaherMundherYaseen2019_ImplementationofUnivariateParadigmforStreamflowSimulation.pdf Yaseen, Zaher Mundher and Wan Mohtar, Wan Hanna Melini and Ameen, Ameen Mohammed Salih and Ebtehaj, Isa and Mohd. Razali, Siti Fatin and Bonakdari, Hossein and Salih, Sinan Q. and Al-Ansari, Nadhir and Shahid, Shamsuddin (2019) Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region. IEEE Access, 7 . pp. 74471-74481. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2019.2920916 DOI:10.1109/ACCESS.2019.2920916 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Yaseen, Zaher Mundher Wan Mohtar, Wan Hanna Melini Ameen, Ameen Mohammed Salih Ebtehaj, Isa Mohd. Razali, Siti Fatin Bonakdari, Hossein Salih, Sinan Q. Al-Ansari, Nadhir Shahid, Shamsuddin Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region |
| title | Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region |
| title_full | Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region |
| title_fullStr | Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region |
| title_full_unstemmed | Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region |
| title_short | Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region |
| title_sort | implementation of univariate paradigm for streamflow simulation using hybrid data driven model case study in tropical region |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/89503/1/ZaherMundherYaseen2019_ImplementationofUnivariateParadigmforStreamflowSimulation.pdf |
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