Monthly inflow forecasting utilizing advanced artificial intelligence methods: A case study of Haditha Dam in Iraq
Document Type
Article
Publication Date
11-1-2021
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).
Keywords
Inflow forecasting, Semi-arid region, Artificial intelligence models, Data splitting
Divisions
sch_civ
Publication Title
Stochastic Environmental Research and Risk Assessment
Volume
35
Issue
11
Publisher
Springer
Publisher Location
ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES