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

Mohammadi, Babak LU orcid ; Moazenzadeh, Roozbeh ; Christian, Kevin and Duan, Zheng LU (2021) In Environmental Science and Pollution Research
Abstract

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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Hydrological modeling, Runoff, Streamflow, machine learning, Water Resources, Watershed
in
Environmental Science and Pollution Research
publisher
Springer
external identifiers
  • pmid:34319517
  • scopus:85111528332
ISSN
0944-1344
DOI
10.1007/s11356-021-15563-1
language
English
LU publication?
yes
id
4c7f15c6-0c43-423f-ad98-c3e839dd8ef2
date added to LUP
2021-08-09 09:43:11
date last changed
2022-05-24 07:25:47
@article{4c7f15c6-0c43-423f-ad98-c3e839dd8ef2,
  abstract     = {{<p>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 m<sup>3</sup> s<sup>−1</sup>, 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.</p>}},
  author       = {{Mohammadi, Babak and Moazenzadeh, Roozbeh and Christian, Kevin and Duan, Zheng}},
  issn         = {{0944-1344}},
  keywords     = {{Hydrological modeling; Runoff; Streamflow; machine learning; Water Resources; Watershed}},
  language     = {{eng}},
  publisher    = {{Springer}},
  series       = {{Environmental Science and Pollution Research}},
  title        = {{Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models}},
  url          = {{http://dx.doi.org/10.1007/s11356-021-15563-1}},
  doi          = {{10.1007/s11356-021-15563-1}},
  year         = {{2021}},
}