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Realistic daily discharge modelling in data-deficient regions using DL-assisted, parametrically-optimized
Imee V Necesito1, Junhyeong Lee2, Seonuk Baek2
1Institute of Water Resources System, Inha University, Incheon, South Korea. ivnecesito@inha.ac.kr.
Scientific Reports
|December 18, 2025
Summary
Deep learning (DL) models show promise in hydrological science but have limitations. Hybrid models combining DL with traditional methods offer the most reliable streamflow simulations, especially in data-scarce regions.
Area of Science:
- Hydrological Science
- Deep Learning Applications
- Water Resource Management
Background:
- Deep learning (DL) is rapidly advancing hydrological science, but its limitations are not fully understood.
- Evaluating DL models alongside traditional and hybrid approaches is crucial for streamflow simulation, particularly in data-deficient areas.
Purpose of the Study:
- To assess the efficacy of DL-based, traditional (HEC-HMS, GR4J), and hybrid hydrological models for streamflow simulation.
- To compare model performance in data-scarce regions, focusing on peak and low flow estimation.
Main Methods:
- Modeled daily discharge in four Samar, Philippines sub-catchments using HEC-HMS, Univariate Long-Short Term Memory (LSTM) Network, Classical GR4J, and a DL-assisted GR4J.
- Parametrically optimized the DL-assisted GR4J model.
Main Results:
- Classical GR4J underestimated discharge; LSTM overestimated peaks.
- The DL-assisted GR4J demonstrated superior performance (NSE: 0.63-0.84, IA: 0.85-0.93) with balanced peak and low flow estimation.
- Univariate LSTM captured trends but missed some peaks, despite high overall metrics.
Conclusions:
- Neither standalone DL nor traditional models are sufficient for optimal hydrological simulation.
- DL-assisted hybrid models provide more reliable and realistic streamflow simulations, crucial for data-deficient and climate-sensitive regions.
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