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 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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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Methodology, writing original draft, software [Babak Mohammadi]; conceptualization, writing—review and editing, software, formal analysis [Roozbeh Moazenzadeh]; data preparing, data curation, writing original draft [Kevin Christian]; methodology, supervision [Zheng Duan]
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Highlights
• Monthly streamflow was simulated in four sub-basins in Indonesia
• Process-driven (HBV, NRECA) and AI-based models (ANFIS, SVM, GMDH) were used
• Hybrid models: combining process-driven and AI-based models
• Less error and better peak value estimation by hybrid models, especially GMDH
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Mohammadi, B., Moazenzadeh, R., Christian, K. et al. Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models. Environ Sci Pollut Res 28, 65752–65768 (2021). https://doi.org/10.1007/s11356-021-15563-1
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DOI: https://doi.org/10.1007/s11356-021-15563-1