A multivariate streamflow forecasting model by integrating improved complete ensemble empirical mode decomposition with additive noise, sample entropy, Gini index and sequence-to-sequence approaches

APAYDIN H., Sibtain M.

JOURNAL OF HYDROLOGY, cilt.603, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 603
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.jhydrol.2021.126831
  • Dergi Adı: JOURNAL OF HYDROLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Environment Index, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Streamflow time series, ICEEMDAN, Seq2Seq, Encode-decode, LSTM, Deep learning, RNN, Hybrid model, HYBRID MODEL, SOHU STREAM, PREDICTION, EMD, OPTIMIZATION, PERFORMANCE
  • Ankara Üniversitesi Adresli: Evet

Özet

Accurate and reliable streamflow forecasting is indispensable to deal with the dynamics of streamflow parameters and for optimal use of water resources, flood, and drought control. In this study, a hybrid-forecasting model integrating improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN)Sample Entropy (SE)-Gini Index (GI)-Sequence to Sequence (Seq2Seq) for streamflow forecasting, namely ISGS (ICEEMDAN-SE-GI-Seq2Seq), is proposed. Firstly, ICEEMDAN is employed to decompose the streamflow time series into subcomponents that are further combined based on SE values. Afterward, GI is used to select the most appropriate features to decrease the computational burden and ameliorate the model's performance. Finally, Seq2Seq architecture based on the long short-term memory (LSTM) algorithm is utilized for predicting the multivariate hydro-meteorological data at three stations located in Turkey. Besides ISGS, the ICEEMDAN-SESeq2Seq, DWT-GI-Seq2Seq, DWT-Seq2Seq, and standalone Seq2Seq models are also constructed as counterpart comparison models. The simulation under different scenarios, several statistical metrics, and judicious plots; substantiate ISGS validity for streamflow forecasting. The correlation coefficient, Nash-Sutcliffe coefficient of efficiency, and Willmott's index values presented by the ISGS model are 0.974, 0.949, and 0.987 for Kirkgoze station, 0.928, 0.839, and 0.947 Kosk station, 0.888, 0.769, and 0.931 for Karagobek station. The performance revealed by the proposed ISGS model proves its viability for streamflow forecasting, along with equal applicability for the other time-series forecasting tasks.