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Babak Mohammadi

Doctoral student

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Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models

Author

  • Babak Mohammadi
  • Roozbeh Moazenzadeh
  • Kevin Christian
  • Zheng Duan

Summary, in English

Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska Byråns Vattenbalansavdelning, and NRECA: Non Recorded Catchment Areas) and seven hybrid models based on three artificial intelligence (AI) methods (adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and group method of data handling (GMDH)) in simulating streamflow in four river basins in Indonesia. HBV and NRECA were developed based on precipitation data. Various combinations of 1-month lagged precipitation data together with outputs of HBV and NRECA were used for developing ANFIS and SVM models, and the best results of ANFIS and SVM formed the inputs to GMDH. Results showed that AI-based hybrid models have generally led to more accurate streamflow estimates compared with HBV and NRECA, and the GMDH model had the best performance at Cipero, Kedungdowo, Notog, and Sukowati stations, with RMSEs of 12.21, 6.07, 20.35, and 24.2 m3 s−1, respectively. More accurate estimation of peak values in training set at Cipero and Sukowati stations, and in both training and testing sets at Kedungdowo station was another advantage of GMDH. Hybrid models based on AI methods can be suitable alternatives to hydrological models, particularly in watersheds where there is a lack of measured data (e.g. climatic parameters, land cover-plant growth data, soil data, stream conditions, and properties of groundwater aquifers), provided that appropriate inputs are used.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • MERGE: ModElling the Regional and Global Earth system

Publishing year

2021

Language

English

Pages

65752-65768

Publication/Series

Environmental Science and Pollution Research

Document type

Journal article

Publisher

Springer

Topic

  • Physical Geography
  • Water Engineering

Keywords

  • Hydrological modeling
  • Runoff
  • Streamflow
  • machine learning
  • Water Resources
  • Watershed

Status

Published

ISBN/ISSN/Other

  • ISSN: 0944-1344