A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates

• Published in: Water (Basel) 2022-01, Vol.14 (1), p.80
• Main Authors: Kilinc, Huseyin Cagan, Haznedar, Bulent
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Aquatic resources; Artificial intelligence; Benchmarks; Climate change; Computational linguistics; Creeks & streams; Deep learning; Drought; Droughts; Estimates; Floods; Flow measurement; Forecasting; Forecasts and trends; Fourier transforms; Genetic algorithms; Global warming; Hydrology; Language processing; Long short-term memory; Management; Model accuracy; Modelling; Natural language interfaces; Neural networks; Optimization techniques; Precipitation; Predictions; Rain; Resource management; River flow; Rivers; Statistical analysis; Statistical methods; Statistics; Stream discharge; Stream flow; Streamflow; Streamflow forecasting; Sustainability; Time series; Water; Water management; Water resources; Water resources management; Water shortages; Wavelet transforms
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w14010080

River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of water resources. Artificial neural networks and deep learning-based hybrid models have been used by scientists in river flow predictions. Therefore, in this study, we propose a hybrid approach, integrating long-short-term memory (LSTM) networks and a genetic algorithm (GA) for streamflow forecasting. The performance of the hybrid model and the benchmark model was taken into account using daily flow data. For this purpose, the daily river flow time series of the Beyderesi-Kılayak flow measurement station (FMS) from September 2000 to June 2019 and the data from Yazıköy from December 2000 to June 2018 were used for flow measurements on the Euphrates River in Turkey. To validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the two FMSs. Statistical methods such as linear regression was used during the comparison process to assess the proposed method’s performance and to demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, STD and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the LSTM-GA model provided promising accuracy results and mainly presented higher performance than the benchmark model and the linear regression model.