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Aquifer-specific flood forecasting using machine learning: A comparative analysis for three distinct sedimentary

Ali J Ali1, Ashraf A Ahmed1

  • 1Department of Civil and Environmental Engineering, Brunel University London, Uxbridge, UB8 3PH, United Kingdom.

The Science of the Total Environment
|October 30, 2025
PubMed
Summary

Machine learning models show varying flood prediction accuracy based on UK aquifer geology. Limestone aquifers are highly predictable, while Greensand aquifers present significant modeling challenges due to complex groundwater-river interactions.

Keywords:
Deep learningFlood risk managementGroundwater–river interactionMulti-horizon forecastingTemporal fusion transformer

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Area of Science:

  • Hydrology and Hydrogeology
  • Geological Sciences
  • Machine Learning Applications

Background:

  • Accurate flood prediction is crucial for mitigating catastrophic impacts, yet its accuracy is influenced by geological conditions.
  • Understanding aquifer-specific groundwater-river dynamics is essential for effective flood forecasting.

Purpose of the Study:

  • To evaluate the performance of four machine learning models (TFT, Informer, LSTM, XGBoost) for multi-horizon flood forecasting (1-4 days).
  • To assess the impact of different aquifer types (Limestone, Chalk, Greensand) in the Thames Basin, UK, on flood prediction accuracy.
  • To investigate the relationship between subsurface hydrology and the reliability of machine learning-based flood forecasting.

Main Methods:

  • Selected hydrological stations based on UK flood risk maps, geological data, and Environment Agency hydrological data.
  • Employed four machine learning models: Transformer (TFT), Informer, Long Short-Term Memory (LSTM), and XGBoost.
  • Analyzed model performance using R-squared values and correlation coefficients (r) to assess groundwater-river linkages.

Main Results:

  • Model accuracy varied significantly across aquifer types: Limestone (R 2 = 0.98-0.99), Chalk (R 2 = 0.77-0.80), and Greensand (low or negative R 2).
  • Transformer and LSTM models outperformed XGBoost, particularly in Limestone aquifers with rapid groundwater level (GWL)-river interactions.
  • Correlation analysis confirmed strong GWL-river linkage in Limestone (r=0.84), moderate in Chalk (r=0.26), and weak/negative in Greensand (r=-0.14).

Conclusions:

  • Subsurface geology significantly impacts the reliability of machine learning-based flood forecasting.
  • Forecasting frameworks must be adapted to specific geological environments for improved flood risk management and resilience planning.
  • Integrating groundwater level data with advanced transformer architectures offers a more physically consistent approach to early flood warning systems.