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April 17, 2026Scientific ReportsOpen Access

Estuarine salinity prediction using empirical mode decomposition and random forest for supporting water resource management

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Authors

ZKZheng KangHHHanliang HuangJZJingwen Zhang

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Overview

Demonstrates improved salinity forecasting in estuaries using EMD and Random Forest, suggesting better water management strategies.

Key Points

  • The aim is to accurately predict estuarine salinity using a hybrid model that integrates EMD and Random Forest.
  • Employs Empirical Mode Decomposition (EMD) to analyze salinity time series.
  • Integrates EMD with Random Forest for enhanced forecasting.
  • Compares various hybrid models based on input and output decomposition strategies.
  • The EMD-RF hybrid model shows significantly improved accuracy over traditional methods.
  • The XY-ANN framework achieves the highest predictive accuracy with an NSE of 0.91.
  • Identifies upstream runoff and tidal factors as key predictors for salinity.

Cite This Study

Kang et al. (2026) studied this question.

synapsesocial.com/papers/69e1cf985cdc762e9d85887ehttps://doi.org/10.1038/s41598-026-48012-5
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