Tofiq, Yahia Mutalib and Latif, Sarmad Dashti and Ahmed, Ali Najah and Kumar, Pavitra and El-Shafie, Ahmed (2022) Optimized model inputs selections for enhancing river streamflow forecasting accuracy using different artificial intelligence techniques. WATER RESOURCES MANAGEMENT, 36 (15). pp. 5999-6016. ISSN 1573-1650, DOI https://doi.org/10.1007/s11269-022-03339-2.
Full text not available from this repository.Abstract
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), R-2 (0.9012). The input combination for the optimum RF model was Q(t-1), Q(t-11), and Q(t-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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Streamflow prediction; Aswan High Dam; Artificial Neural Network; Support Vector Machine; Random Forest; Boosted Tree Regression |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Koh Ai Peng |
| Date Deposited: | 25 Jul 2024 01:44 |
| Last Modified: | 25 Jul 2024 01:44 |
| URI: | http://eprints.um.edu.my/id/eprint/46240 |
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