Data-Driven Modeling of Groundwater Level Using Machine Learning

Abstract

This US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory engineering technical note (CHETN) documents a preliminary study on the use of specialized machine learning (ML) methods to model the variations in groundwater level (GWL) with time. This approach uses historical groundwater observation data at seven gage locations in Wyoming, USA, available from the USGS database and historical data on several relevant meteorological variables obtained from the ERA5 reanalysis dataset produced by the Copernicus Climate Change Service (usually referred to as C3S) at the European Center for Medium-Range Weather Forecasts to predict future GWL values for a desired period of time. The results presented in this report indicate that the ML method has the potential to predict both short-term (4-hourly) as well as daily variations in GWL several days into the future for the chosen study region, thus alleviating the need for employing sophisticated process-based numerical models with complicated model structure configurations.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2024
Accession Number
AD1227313

Entities

People

  • A. M. Wagner
  • Nawa R. Pradhan
  • Sourav Dutta
  • Theadora K. Hall

Organizations

  • Engineer Research and Development Center

Tags

Fields of Study

  • Environmental science

Readers

  • Climatology
  • Coastal and Marine Engineering/Sediment Transport/Hydraulic Engineering
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks
  • Fully Networked C3
  • Fully Networked C3 - Command and Control