2026-10-18T23:54:04Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/113282026-10-18T20:41:23Zopenairedrivercom_ebyu_10000col_ebyu_10001

An evolutionary hybrid method based on particle swarm optimization algorithm and extreme gradient boosting for short-term streamflow forecastingKilinc, Huseyin CaganHaznedar, BulentOzkan, FurkanKatipoğlu, Okan MertMachine learningMetaheuristic optimization algorithmPredictionProposed modelStreamflowEvros RiverBenchmarkingDecision makingLong short-term memoryParticle swarm optimization (PSO)RiversStatistical testsStream flowWater conservationWater managementGradient boostingMachine-learningMetaheuristic optimizationOptimization algorithmsParticle swarmParticle swarm optimization algorithmRiver flowalgorithmforecasting methodhydrological modelingoptimizationForecastingStreamflow 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 (R2) values. The forecasting results of the models were evaluated with statistical metrics. The proposed PSO-XGBoost model outperformed all models with R2 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. © The Author(s), under exclusive licence to Institute of Geophysics of the Polish Academy of Sciences 2024.Springer Science and Business Media Deutschland GmbH2024Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/11328https://doi.org/10.1007/s11600-024-01307-51895-6572enActa Geophysicainfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/11328