Babak Mohammadi
Doktorand
Application of novel artificial bee colony optimized ANN and data preprocessing techniques for monthly streamflow estimation
Författare
Summary, in English
Streamflow estimation is important in hydrology, especially in drought and flood-prone areas. Accurate estimation of streamflow values is crucial for the sustainable management of water resources, the development of early warning systems for disasters, and for various applications such as irrigation, hydropower production, dam sizing, and siltation management. This study developed the ANN algorithm by optimizing with an artificial bee colony (ABC). Then, the ABC-ANN hybrid model, which was established, was combined with different signal decomposition techniques to evaluate its performance in streamflow estimation in the East Black Sea Region, Türkiye. For this purpose, the lagged streamflow values were divided into subcomponents using the local mean decomposition (LMD) with the empirical envelope and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) signal decomposition techniques presented to the ABC-ANN algorithm. Thus, the success of the novel hybrid LMD-ABC-ANN and CEEMDAN-ABC-ANN approaches in streamflow prediction was evaluated. The outputs are reliable strategies and resources for water resource planners and policymakers.
Avdelning/ar
- Institutionen för naturgeografi och ekosystemvetenskap
Publiceringsår
2023
Språk
Engelska
Sidor
89705-89725
Publikation/Tidskrift/Serie
Environmental Science and Pollution Research
Volym
30
Issue
38
Dokumenttyp
Artikel i tidskrift
Förlag
Springer
Ämne
- Civil Engineering
Nyckelord
- Artificial bee colony optimization
- East Black Sea Region
- Empirical mode decomposition
- Local mean decomposition
- Streamflow prediction
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 0944-1344