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Related Experiment Videos

Enhancing flood prediction through physics-driven typhoon feature engineering and machine learning.

Zhi Zhang1, Yusha Xiao2, Biqing Chen1

  • 1Tourism and Historical Culture College, Zhaoqing University, Zhaoqing, China.

Plos One
|April 20, 2026
PubMed
Summary
This summary is machine-generated.

A new physics-informed machine learning model improves typhoon flood prediction in subtropical watersheds by integrating typhoon physics. This enhances accuracy, especially during extreme events, aiding disaster preparedness.

Related Experiment Videos

Area of Science:

  • Hydrology
  • Meteorology
  • Machine Learning

Background:

  • Typhoon-induced floods threaten subtropical watersheds.
  • Limited integration of typhoon physics in machine learning flood prediction.

Purpose of the Study:

  • Develop a physics-informed machine learning framework for flood prediction in the Boluo watershed.
  • Emphasize typhoon feature engineering for improved accuracy.

Main Methods:

  • Evaluated four models (LR, ANN, RF, XGB) across three scenarios: Baseline, With Typhoon, and Enhanced Typhoon (physics-informed features).
  • Designed physics-driven features including distance decay functions, cumulative impact indices, and trajectory-based kinematic features.

Main Results:

  • Artificial Neural Network with Enhanced Typhoon features (ANN-EnTY) achieved superior performance (KGE=0.946, RMSE=174 m³/s).
  • ANN-EnTY improved KGE by 3.1% and reduced RMSE by 16.7 m³/s compared to the Baseline.
  • Typhoon features played critical synergistic roles during extreme events, despite a 2% overall contribution.

Conclusions:

  • The physics-informed framework offers a replicable methodology for typhoon-prone watersheds.
  • Dual-mode response patterns (routine vs. extreme events) provide mechanistic insights for flood warning systems.
  • The study has direct implications for disaster preparedness and water management.