An evolutionary hybrid method based on particle swarm optimization algorithm and extreme gradient boosting for short-term streamflow forecasting

• Published in: Acta geophysica 2024, Vol.72 (5), p.3661-3681
• Main Authors: Kilinc, Huseyin Cagan, Haznedar, Bulent, Ozkan, Furkan, Katipoğlu, Okan Mert
• Format: Article
• Language: English
• Subjects: Algorithms; Daily forecasts; Data analysis; Datasets; Earth and Environmental Science; Earth Sciences; Evolutionary algorithms; Forecasting; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Heuristic methods; Particle swarm optimization; Regression models; Research Article - Hydrology and Hydraulics; River basins; River flow; River forecasting; Rivers; Statistical analysis; Stream discharge; Stream flow; Streamflow estimation; Streamflow forecasting; Structural Geology; Sustainability management; Water consumption; Water management; Water resources management; Water users
• ISSN: 1895-7455; 1895-6572; 1895-7455
• DOI: 10.1007/s11600-024-01307-5

Streamflow estimation is necessary to develop sustainable water management strategies that balance the needs of various water users while protecting the ecological health of the watershed. This study presents a comprehensive analysis of river flow data in Turkey's Meriç basin using long-term daily data spanning the years 2001–2011. Within the scope of forecasting of daily river flows, 80% of dataset was used for training stage and the 20% of dataset was used for testing. The performance of four different models, including Extreme Gradient Boosting (XGBoost) benchmark model, Linear Regression, Long Short-Term Memory Network (LSTM), and proposed Particle Swarm Optimization (PSO)—XGBoost hybrid model, was evaluated using several statistical evaluation criteria such as RMSE, MAPE, MAE, SD, NSE, KGE , and model success was assessed based on determination coefficient ( R 2 ) values. The forecasting results of the models were evaluated with statistical metrics. The proposed PSO-XGBoost model outperformed all models with R 2 values of 0.7460 at the Babaeski FMS station, 0.9582 at the Meriç FMS station, and 0.8116 at the Hayrabolu FMS station, respectively. It has been found that when the parameters of the XGBoost model are optimized with the meta-heuristic PSO algorithm, the daily flow prediction accuracy is significantly increased in the Meriç River basin, which has a pluviozonal River regime. The results demonstrate that the proposed hybrid model produces precise and reliable estimations for daily river flow analysis in the Meriç basin. The findings derived from the proposed model demonstrate its promising potential for water resource management and decision-making in the region.