Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow
Estimating the reliability of potential prediction is very crucial as our life depended heavily on it. Thus, a simulation that concerned hydrological factors such as streamflow must be enhanced. In this study, Autoregressive (AR) and Artificial Neural Networks (ANN) were used. The forecasting result...
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UAD INSTITUTE OF SCIENTIFIC PUBLICATION AND PRESS
2020
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| Online Access: | http://eprints.uthm.edu.my/6672/ http://doi.org/10.11591/ijai.v9.i3.pp464-472 |
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| author | Wan Zurey, Wan Nur Hawa Fatihah Ismail, Shuhaida Mustapha, Aida |
| author_facet | Wan Zurey, Wan Nur Hawa Fatihah Ismail, Shuhaida Mustapha, Aida |
| author_sort | Wan Zurey, Wan Nur Hawa Fatihah |
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| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Estimating the reliability of potential prediction is very crucial as our life depended heavily on it. Thus, a simulation that concerned hydrological factors such as streamflow must be enhanced. In this study, Autoregressive (AR) and Artificial Neural Networks (ANN) were used. The forecasting result for each model was assessed by using various performance measurements such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE) and Nash-Sutcliffe Model Efficiency Coefficient (CE). The experimental results showed the forecast performance of Durian Tunggal reservoir datasets by using ANN Model 7 with 7 hidden neurons has better forecast performance compared to AR (4). The ANN model has the smallest MAE (0.0116 m3/s), RMSE (0.0607 m3/s), MAPE (1.8214% m3/s), MFE (0.0058 m3/s) and largest CE (0.9957 m3/s) which show the capability of fitting to a nonlinear dataset. In conclusion, high predictive precision is an advantage as a proactive or precautionary measure that can be inferred in advance in order to avoid certain negative effects. |
| format | Article |
| id | my.uthm.eprints-6672 |
| institution | Universiti Tun Hussein Onn Malaysia |
| publishDate | 2020 |
| publisher | UAD INSTITUTE OF SCIENTIFIC PUBLICATION AND PRESS |
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| spelling | my.uthm.eprints-66722022-03-14T01:49:31Z http://eprints.uthm.edu.my/6672/ Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow Wan Zurey, Wan Nur Hawa Fatihah Ismail, Shuhaida Mustapha, Aida GB Physical geography Estimating the reliability of potential prediction is very crucial as our life depended heavily on it. Thus, a simulation that concerned hydrological factors such as streamflow must be enhanced. In this study, Autoregressive (AR) and Artificial Neural Networks (ANN) were used. The forecasting result for each model was assessed by using various performance measurements such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE) and Nash-Sutcliffe Model Efficiency Coefficient (CE). The experimental results showed the forecast performance of Durian Tunggal reservoir datasets by using ANN Model 7 with 7 hidden neurons has better forecast performance compared to AR (4). The ANN model has the smallest MAE (0.0116 m3/s), RMSE (0.0607 m3/s), MAPE (1.8214% m3/s), MFE (0.0058 m3/s) and largest CE (0.9957 m3/s) which show the capability of fitting to a nonlinear dataset. In conclusion, high predictive precision is an advantage as a proactive or precautionary measure that can be inferred in advance in order to avoid certain negative effects. UAD INSTITUTE OF SCIENTIFIC PUBLICATION AND PRESS 2020 Article PeerReviewed Wan Zurey, Wan Nur Hawa Fatihah and Ismail, Shuhaida and Mustapha, Aida (2020) Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow. IAES International Journal of Artificial Intelligence, 9 (3). pp. 464-472. ISSN 2252-8938 http://doi.org/10.11591/ijai.v9.i3.pp464-472 |
| spellingShingle | GB Physical geography Wan Zurey, Wan Nur Hawa Fatihah Ismail, Shuhaida Mustapha, Aida Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow |
| title | Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow |
| title_full | Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow |
| title_fullStr | Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow |
| title_full_unstemmed | Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow |
| title_short | Forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow |
| title_sort | forecasting accuracy: a comparative study between artificial neural network and autoregressive model for streamflow |
| topic | GB Physical geography |
| url | http://eprints.uthm.edu.my/6672/ http://doi.org/10.11591/ijai.v9.i3.pp464-472 |
| url_provider | http://eprints.uthm.edu.my/ |