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December 19, 2025VIETNAM JOURNAL OF EARTH SCIENCES

Reservoir inflow forecasting using Voting Ensemble model: A case study at A Luoi hydropower, central Vietnam

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Authors

CNChi NguyenVDViet Long DoanTNTrung Quan Nguyen

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Overview

Voting Ensemble model shows improved reservoir inflow predictions, enhancing water management and flood mitigation.

Key Points

  • This research aims to develop a weighted Voting Ensemble model for forecasting reservoir inflow.
  • Utilized multi-station rainfall and lagged inflow data for daily inflow predictions.
  • Trained various machine learning models including RF and others on a unified feature set.
  • Combined models using performance-based weights from time-series cross-validation errors.
  • The ensemble model reduced RMSE by 12–25% compared to standalone models.
  • Achieved a maximum NSE of 0.92 during testing, compared to 0.70–0.91 for individual models.
  • Maintained strong performance (NSE ≈ 0.98) during independent verification, effectively predicting flow patterns.

Cite This Study

Nguyen et al. (2025) studied this question.

synapsesocial.com/papers/69449a892f0218eca950844bhttps://doi.org/10.15625/2615-9783/23973
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Context-Aware Localized Weighting Ensemble Model for Reservoir Inflow Forecasting2026
  2. 2Dynamic Flood Risk Assessment in Shenzhen Integrating Ensemble Voting Algorithms and Machine Learning2026 · 1 citations
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  4. 4Research on a Hydropower Station Tailwater Level Prediction Method Based on Stacked Ensemble Learning2026
  5. 5AI-Based Time-Series Ensemble Approach Coupled with a Hydrological Model for Reservoir Storage Prediction in Korea2025