Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques
Dams; Decision trees; Forecasting; Forestry; Information management; Irrigation; Reservoir management; Reservoirs (water); Stream flow; Support vector machines; Water management; Water supply; Aswan high dam; Boosted tree regression; High dams; Inflow predictions; Intelligence models; Random forest...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Springer Science and Business Media B.V.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833413361274978304 |
|---|---|
| author | Tofiq Y.M. Latif S.D. Ahmed A.N. Kumar P. El-Shafie A. |
| author2 | 57924167700 |
| author_facet | 57924167700 Tofiq Y.M. Latif S.D. Ahmed A.N. Kumar P. El-Shafie A. |
| author_sort | Tofiq Y.M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Dams; Decision trees; Forecasting; Forestry; Information management; Irrigation; Reservoir management; Reservoirs (water); Stream flow; Support vector machines; Water management; Water supply; Aswan high dam; Boosted tree regression; High dams; Inflow predictions; Intelligence models; Random forest modeling; Random forests; Streamflow prediction; Support vectors machine; Tree regression; Neural networks; accuracy assessment; algorithm; artificial intelligence; artificial neural network; forecasting method; numerical model; regression analysis; river flow; streamflow; support vector machine; Aswan Dam; Aswan [Egypt]; Egypt |
| format | Article |
| id | my.uniten.dspace-26648 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Springer Science and Business Media B.V. |
| record_format | dspace |
| spelling | my.uniten.dspace-266482023-05-29T17:36:02Z Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques Tofiq Y.M. Latif S.D. Ahmed A.N. Kumar P. El-Shafie A. 57924167700 57216081524 57214837520 57206939156 16068189400 Dams; Decision trees; Forecasting; Forestry; Information management; Irrigation; Reservoir management; Reservoirs (water); Stream flow; Support vector machines; Water management; Water supply; Aswan high dam; Boosted tree regression; High dams; Inflow predictions; Intelligence models; Random forest modeling; Random forests; Streamflow prediction; Support vectors machine; Tree regression; Neural networks; accuracy assessment; algorithm; artificial intelligence; artificial neural network; forecasting method; numerical model; regression analysis; river flow; streamflow; support vector machine; Aswan Dam; Aswan [Egypt]; Egypt The development of a river inflow prediction is a prerequisite for dam reservoir management. Precise�forecasting leads to better irrigation water management, reservoir operation refinement, enhanced hydropower output and mitigation of risk of natural hazards such as flooding. Dam created reservoirs prove to be an essential source of water in arid and semi-arid regions. Over the years, Artificial Intelligence (AI) has been used for development of models for prediction of various natural variables in different engineering fields. Also, several AI models have been proved to be beneficial over the conventional models in efficient prediction of various natural variables. In this study, four AI models, namely, Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Boosted Tree Regression (BTR) were developed and trained over 130-years�of monthly historical rainfall data to forecast streamflow at Aswan High Dam, Egypt. The input parameters were selected according to the Autocorrelation Function (ACF) plot. The findings revealed that RF model outperformed other techniques and could provide precise�monthly streamflow prediction with the lowest RMSE (2.2395) and maximum WI (0.998462), R2 (0.9012). The input combination for the optimum RF model was Qt-1, Qt-11, and Qt-12 (i.e., one-, eleven- and twelve-months delay inputs). The optimum RF model provides a reliable source of data for inflow predictions, which allows improved utilization of water resources and long-term water resource planning and management. � 2022, The Author(s), under exclusive licence to Springer Nature B.V. Final 2023-05-29T09:36:01Z 2023-05-29T09:36:01Z 2022 Article 10.1007/s11269-022-03339-2 2-s2.0-85139615172 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139615172&doi=10.1007%2fs11269-022-03339-2&partnerID=40&md5=4a352e723cedd79db8217e9d71b32c06 https://irepository.uniten.edu.my/handle/123456789/26648 36 15 5999 6016 Springer Science and Business Media B.V. Scopus |
| spellingShingle | Tofiq Y.M. Latif S.D. Ahmed A.N. Kumar P. El-Shafie A. Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques |
| title | Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques |
| title_full | Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques |
| title_fullStr | Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques |
| title_full_unstemmed | Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques |
| title_short | Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques |
| title_sort | optimized model inputs selections for enhancing river streamflow forecasting accuracy using different artificial intelligence techniques |
| url_provider | http://dspace.uniten.edu.my/ |