ENSEMBLE MACHINE LEARNING FOR GLOBAL HYDROLOGICAL PREDICTION
DOI:
https://doi.org/10.37943/24DKYV6003Keywords:
hydrological modeling, machine learning, ensemble learning, discharge prediction, water resources monitoringAbstract
Accurate global hydrological prediction is vital for sustainable water management but is often hindered by data complexity and fragmentation. This study introduces an advanced machine learning framework to predict long-term average discharge using widely available global hydrological station metadata, aiming to develop a highly accurate and generalizable model for large-scale water resource assessment. The methodology utilized the Global Runoff Data Centre (GRDC) dataset, applying extensive feature engineering to station characteristics and a logarithmic transformation to the discharge variable. A diverse set of algorithms was trained, including a custom deep neural network with specialized architecture and several gradient boosting machines. These individual models were then integrated into a final Meta Ensemble model through an optimized weighting strategy to maximize predictive performance. The framework was rigorously validated on an independent test set. The Meta Ensemble model demonstrated superior predictive power, achieving a Coefficient of Determination (R²) of 0.954. This performance significantly surpassed that of both baseline methods and the individual advanced models. Analysis of the results confirmed that the model learned hydrologically meaningful relationships, identifying catchment area and geographical location as the most influential predictors. The findings confirm that a data-driven ensemble framework can accurately predict key hydrological characteristics using only station metadata. This approach offers a powerful and scalable alternative to traditional modeling, holding significant potential for water resource assessment in data-scarce regions and serving as a robust foundation for future intelligent monitoring systems.
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