Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq
| Autor: | Mohammed Falah Allawi, Ahmed El-Shafie, Majid Ibrahim Salman, Intesar Razaq Hussain |
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| Rok vydání: | 2021 |
| Předmět: |
Adaptive neuro fuzzy inference system
Environmental Engineering Mean squared error Computer science Computational intelligence Inflow Reservoir operation Water resources Approximation error Statistics Environmental Chemistry Safety Risk Reliability and Quality Reliability (statistics) General Environmental Science Water Science and Technology |
| Zdroj: | Stochastic Environmental Research and Risk Assessment. 35:2391-2410 |
| ISSN: | 1436-3259 1436-3240 |
| DOI: | 10.1007/s00477-021-02052-7 |
| Popis: | 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 m3/s), a RMSE (78.10 m3/s) and a high correlation between the actual and forecasted data (R2 = 0.97). |
| Databáze: | OpenAIRE |
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