Comparative analysis of river flow modelling by using supervised learning technique
The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM),...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6974/1/P9860_86c30fe4c3819f7c20bebe82732ee268.pdf |
| _version_ | 1825710289617682432 |
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| author | Ismail, Shuhaida Pandiahi, Siraj Mohamad Shabri, Ani Mustapha, Aida |
| author_facet | Ismail, Shuhaida Pandiahi, Siraj Mohamad Shabri, Ani Mustapha, Aida |
| author_sort | Ismail, Shuhaida |
| collection | UTHM |
| description | The goal of this research is to investigate the efficiency of three supervised learning
algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550
square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector
Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The
forecasting models predict the monthly river flow obtained from the three models individually
for river flow data and the accuracy of the all models were then compared against each other.
The monthly river flow of the said river has been forecasted using these three models. The
obtained results were compared and statistically analysed. Then, the results of this analytical
comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It
was found that LSSVM has he higher r with the value of 0.934 compared to other models. This
indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model. |
| first_indexed | 2024-03-05T21:55:21Z |
| format | Conference or Workshop Item |
| id | uthm.eprints-6974 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:55:21Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | uthm.eprints-69742022-04-24T00:35:01Z http://eprints.uthm.edu.my/6974/ Comparative analysis of river flow modelling by using supervised learning technique Ismail, Shuhaida Pandiahi, Siraj Mohamad Shabri, Ani Mustapha, Aida TD194-195 Environmental effects of industries and plants The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The forecasting models predict the monthly river flow obtained from the three models individually for river flow data and the accuracy of the all models were then compared against each other. The monthly river flow of the said river has been forecasted using these three models. The obtained results were compared and statistically analysed. Then, the results of this analytical comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It was found that LSSVM has he higher r with the value of 0.934 compared to other models. This indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model. 2018 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/6974/1/P9860_86c30fe4c3819f7c20bebe82732ee268.pdf Ismail, Shuhaida and Pandiahi, Siraj Mohamad and Shabri, Ani and Mustapha, Aida (2018) Comparative analysis of river flow modelling by using supervised learning technique. In: ISMAP 2017, October 28, 2017, Batu Pahat, Johor. https://doi.org/10.1088/1742-6596/995/1/012045 |
| spellingShingle | TD194-195 Environmental effects of industries and plants Ismail, Shuhaida Pandiahi, Siraj Mohamad Shabri, Ani Mustapha, Aida Comparative analysis of river flow modelling by using supervised learning technique |
| title | Comparative analysis of river flow modelling by using supervised learning technique |
| title_full | Comparative analysis of river flow modelling by using supervised learning technique |
| title_fullStr | Comparative analysis of river flow modelling by using supervised learning technique |
| title_full_unstemmed | Comparative analysis of river flow modelling by using supervised learning technique |
| title_short | Comparative analysis of river flow modelling by using supervised learning technique |
| title_sort | comparative analysis of river flow modelling by using supervised learning technique |
| topic | TD194-195 Environmental effects of industries and plants |
| url | http://eprints.uthm.edu.my/6974/1/P9860_86c30fe4c3819f7c20bebe82732ee268.pdf |
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