Monthly stream flow prediction: the power of ensemble machine learning-based decision support models

Erdal H., NAMLI E.

INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY, cilt.16, sa.1, ss.17-36, 2023 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1504/ijhst.2023.131834
  • Dergi Adı: INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Compendex, INSPEC, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.17-36
  • İstanbul Üniversitesi Adresli: Hayır

Özet

Predicting stream flow is a vital milestone in planning and managing water resources, and is important to researchers and hydrologists. Unpredicted stream flow threads cultivated areas, dams and riverside lands. Recently, the increasing popularity of machine learning (ML) methods including ensemble methods in hydrological prediction is noticeable. In this study, five single and three ensembles ML-based 18 prediction models and six performance evaluation measurements are utilised for monthly stream flow prediction. It proved that models developed by stacking and voting ensemble ML methods have higher prediction accuracy. As a conclusion, this paper has presented the promising endeavour of incorporating sentiment regression into stream flow prediction.