77 Groundwater and Sustainability: Integrating Science, Technology, and Policy

Groundwater is vital for clean water and food security but faces threats from climate change, population growth, and mismanagement. Integrating science, technology, and policy fosters collaboration, innovation, and effective strategies to ensure its sustainable use for future generations.

Monday, 20 October 2025
8:00 AM - 12:00 PM
HBGCC, 210AB

Deep Learning Approach for Groundwater Level Prediction in Vietnam

Paper number: 77-13
Program: Technical Sessions
Time: 11:05 AM – 11:25 AM

Authors

  • Linh Bui Presenting Author
    Southeast Asia Union for Water, Environment and Geosciences – SEAGU, Hanoi, (None)
  • Duong Bui
    National Center for Water Resources Planning and Investigation - NAWAPI, Hanoi, (None)
  • Prosun Bhattacharya
    KTH ROYAL INSTITUTE OF TECHNOLOGY, Stockholm
  • Lenny Winkel
    ETH Zurich, Zurich
  • Nuong Bui
    Hanoi University of Natural Resources & Environment (HUNRE), Hanoi
  • Hoa Nguyen
    Hanoi University of Mining and Geology, Hanoi
  • Anh Vu
    Hanoi University of Mining and Geology, Hanoi
  • Dan Bui
    Southeast Asia Union for Water, Environment and Geosciences – SEAGU, Hanoi
  • Du Bui
    Southeast Asia Union for Water, Environment and Geosciences – SEAGU, Hanoi

Abstract

This study investigates the application of a Long Short-Term Memory (LSTM) model for multi-day-ahead groundwater level forecasting. Utilizing precipitation data from GM_force and temperature data from the ERA5 reanalysis dataset, we developed and trained models for three distinct monitoring stations: Q.176a, C3a, and LK75T. The models were trained on 180 days of historical data to forecast groundwater levels for lead times of 1 to 7 days. Performance evaluation, based on the Nash-Sutcliffe Efficiency (NSE) coefficient, revealed that the C3a station model yielded the best overall performance, with NSE values between 0.715 and 0.652 across the forecast horizon. While the Q.176a model demonstrated the highest accuracy for a 1-day lead (NSE = 0.724), its performance substantially decreased at the 7-day lead time (NSE = 0.222). Conversely, the LK75T model exhibited moderate yet consistent performance, with NSE scores ranging from 0.540 to 0.399 for all lead times. The findings confirm the utility of ERA5 meteorological data for multi-day groundwater forecasting and suggest that the LK75T model provides the most reliable and stable performance for operational applications among the tested sites.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-413138

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