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, vol.16, no.1, pp.17-36, 2023 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 1
  • Publication Date: 2023
  • Doi Number: 10.1504/ijhst.2023.131834
  • Journal Name: INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Compendex, INSPEC, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.17-36
  • Istanbul University Affiliated: No

Abstract

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.