#7050. Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq

January 2027publication date
Proposal available till 06-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Water Science and Technology;
Environmental Science (all);
Safety, Risk, Reliability and Quality;
Environmental Engineering;
Environmental Chemistry;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №7050.1 Contract №7050.2 Contract №7050.3 Contract №7050.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

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 m3/s), a RMSE (78.10 m3/s) and a high correlation between the actual and forecasted data (R2 = 0.97).
Keywords:
Artificial intelligence models; Data splitting; Inflow forecasting; Semi-arid region

Contacts :
0