Proposed Correlation Model for Groundwater Level Prediction Based on River Stage Considering Changes in Hydrological and Geological Conditions

Incheol Kim, Junhwan Lee

Research output: Contribution to journalArticle

Abstract

The groundwater level (GWL) is an important subsoil condition that affects water resource management, soil contamination analysis, and infrastructure design. In this study, the spatial and time-dependent variation of the GWL was analyzed and a GWL prediction model based on river stage (RS) is proposed. A series of finite-element analyses were performed for the various geological and hydrological conditions. The results were used to quantify the response of the GWL to the RS for various permeability and RS conditions. A correlation model of the GWL and RS was established and the model parameters were evaluated to yield design equations. Implementation steps for the method were derived, including an adjustment procedure for cases with multiple RS fluctuations. Two case studies were used to verify the validity of the method by comparing its predictions with both the measured values and results from an artificial neural network (ANN). The correlation model produced well-predicted results in both the shape and values compared with the measured GWL.

Original languageEnglish
Article number04019042
JournalJournal of Hydrologic Engineering
Volume24
Issue number10
DOIs
Publication statusPublished - 2019 Oct 1

Fingerprint

Groundwater
Rivers
groundwater
prediction
river
Water resources
subsoil
artificial neural network
Contamination
infrastructure
permeability
Neural networks
Soils
soil
method

All Science Journal Classification (ASJC) codes

  • Environmental Chemistry
  • Civil and Structural Engineering
  • Water Science and Technology
  • Environmental Science(all)

Cite this

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abstract = "The groundwater level (GWL) is an important subsoil condition that affects water resource management, soil contamination analysis, and infrastructure design. In this study, the spatial and time-dependent variation of the GWL was analyzed and a GWL prediction model based on river stage (RS) is proposed. A series of finite-element analyses were performed for the various geological and hydrological conditions. The results were used to quantify the response of the GWL to the RS for various permeability and RS conditions. A correlation model of the GWL and RS was established and the model parameters were evaluated to yield design equations. Implementation steps for the method were derived, including an adjustment procedure for cases with multiple RS fluctuations. Two case studies were used to verify the validity of the method by comparing its predictions with both the measured values and results from an artificial neural network (ANN). The correlation model produced well-predicted results in both the shape and values compared with the measured GWL.",
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