Boosting flood routing prediction performance through a hybrid approach using empirical mode decomposition and neural networks: a case study of the Mera River in Ankara

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KATİPOĞLU O. M., SARIGÖL M.

Water Supply, cilt.23, sa.11, ss.4403-4415, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 11
  • Basım Tarihi: 2023
  • Doi Numarası: 10.2166/ws.2023.288
  • Dergi Adı: Water Supply
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Compendex, Environment Index, Geobase, ICONDA Bibliographic, Pollution Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.4403-4415
  • Anahtar Kelimeler: Ankara, cascade forward backpropagation, empirical mode decomposition, feed-forward backpropagation, flood routing, machine learning
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

Flood routing is vital in helping to reduce the impact of floods on people and communities by allowing timely and appropriate responses. In this study, the empirical mode decomposition (EMD) signal decomposition technique is combined with cascade forward backpropagation neural network (CFBNN) and feed-forward backpropagation neural network (FFBNN) machine learning (ML) techniques to model 2014 floods in Ankara, Mera River. The data are split in order to avoid the underfitting and overfitting problems of the algorithm. While establishing the algorithm, 70% of the data were divided into training, 15% testing and 15% validation. Graphical indicators and statistical parameters were used for the analysis of model performance. As a result, the EMD signal decomposition technique has been found to improve the performance of ML models. In addition, the EMD-FFBNN hybrid model showed the most accurate estimation results in the flood routing calculation. The study’s outputs can assist in designing flood control structures such as levees and dams to help reduce flood risk.