Allawi, Mohammed Falah and Hussain, Intesar Razaq and Salman, Majid Ibrahim and El-Shafie, Ahmed (2021) Monthly inflow forecasting utilizing advanced artificial intelligence methods: A case study of Haditha Dam in Iraq. Stochastic Environmental Research and Risk Assessment, 35 (11). pp. 2391-2410. ISSN 1436-3240, DOI https://doi.org/10.1007/s00477-021-02052-7.
Full text not available from this repository.Abstract
Accuracy of reservoir inflow forecasting is an important issue for the reservoir operation and water resources management. The main aim of the current study is to develop reliable models to forecast monthly inflow data. The present research proposed a robust model called co-active neuro-fuzzy inference system (CANFIS) to improve the forecasting accuracy. The reliability of the CANFIS model was evaluated by comparing with two different AI-based models, ANN and ANFIS model. To obtain the best forecasting result, the proposed models were trained utilizing four different Training Procedures. This study was conducted to forecast the inflow data for Haditha Dam on Euphrates River, Iraq. The comparison of models reveals that the CANFIS model is better than ANN and ANFIS model. The results showed that the second training procedure is more suitable for the forecasting models. The CANFIS model yielded a relative error of less than (15%), a low MAE (69.66 m(3)/s), a RMSE (78.10 m(3)/s) and a high correlation between the actual and forecasted data (R-2 = 0.97).
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Inflow forecasting; Semi-arid region; Artificial intelligence models; Data splitting |
| Subjects: | Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Mar 2022 03:13 |
| Last Modified: | 21 Mar 2022 03:13 |
| URI: | http://eprints.um.edu.my/id/eprint/26575 |
Actions (login required)
 |
View Item |
