Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows

KATİPOĞLU O. M., Yeşilyurt S. N., DALKILIÇ H. Y., AKAR F.

Environmental Monitoring and Assessment, cilt.195, sa.9, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 195 Sayı: 9
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s10661-023-11700-0
  • Dergi Adı: Environmental Monitoring and Assessment
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, Greenfile, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Empirical mode decomposition, Least square support vector machine, Monthly streamflow modeling, Particle swarm optimization
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), and particle swarm optimization (PSO) methods in modeling monthly streamflow was evaluated. For establishing the models, 42 years of monthly average streamflow data was used in two hydrometer stations located in the Konya Closed Basin, covering 1964 to 2005. Lagged streamflow values ​​were selected as inputs according to partial autocorrelation values ​​in establishing the models. The dataset was divided into 70% training and 30% testing. Model performances were evaluated according to mean square error, root mean square error, correlation coefficients, scatter plot, and Taylor and Violin diagrams. As a result of the analysis, it was determined that the PSO-LSSVM and EMD-LSSVM models were slightly more successful than the single LSSVM model, and the best model was obtained with the EMD-PSO-LSSVM. In addition, in estimating monthly stream flows, 1-, 9-, 10-, 11-, and 12-month lagged streamflow values were the input combination that gave the best results in semi-arid climatic regions. This result demonstrated that EMD improved the performance of both LSSVM and PSO-LSSVM models by 1% to 5% based on correlation coefficient (R) values.