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Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin.

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Area of Science:

  • Hydrology
  • Water Resource Management
  • Environmental Modeling

Background:

  • Accurate monthly runoff estimation is crucial for water resource planning, especially in ungauged basins.
  • Regionalization methods are vital for transferring hydrological model parameters from gauged to ungauged areas.
  • The GR2M (GR4J) rainfall-runoff model is widely used but requires regional parameter estimation for ungauged catchments.

Purpose of the Study:

  • To evaluate and compare the performance of different regionalization methods for determining GR2M model parameters.
  • To assess the accuracy of estimated regional parameters and their impact on monthly runoff prediction.
  • To identify the most effective regionalization approach for ungauged basins in Thailand's southern region.

Main Methods:

  • Investigated two main regionalization approaches: regression-based (Multiple Linear Regression, Random Forest, M5 Model Tree) and distance-based (Spatial Proximity Approach, Physical Similarity Approach).
  • Analyzed hydrological data and physical attributes from 37 runoff stations in Thailand.
  • Applied estimated regional parameters to the GR2M model for monthly runoff simulation and evaluated performance using Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI).

Main Results:

  • Hydrological data-based regional parameter estimation outperformed physical attribute-based methods.
  • Random Forest (RF) exhibited the highest accuracy in estimating regional GR2M parameters, followed by M5, MLR, SPA, and PSA.
  • Regionalized monthly runoff simulations using RF yielded the best performance, outperforming SPA, M5, MLR, and PSA.

Conclusions:

  • Machine learning techniques, particularly Random Forest, are highly effective for estimating monthly runoff in ungauged basins.
  • The Spatial Proximity Approach (SPA) is a viable alternative when physical and hydrological data are scarce.
  • This research provides valuable insights for improving hydrological modeling and water resource management in data-limited regions.