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The Applicability of Conditional Generative Model Generating Groundwater Level Fluctuation Corresponding to Precipitation Pattern
조건부 생성모델을 이용한 강수 패턴에 따른 지하수위 생성 및 이의 활용에 관한 연구
Econ. Environ. Geol. 2021 Feb;54(1):77-89
Published online February 28, 2021;  https://doi.org/10.9719/EEG.2021.54.1.77
Copyright © 2021 the Korean society of economic and environmental gelology.

Jiho Jeong1, Jina Jeong1,*, Byung Sun Lee2, Sung-Ho Song2
정지호1 · 정진아1,* · 이병선2 · 송성호2

1Department of Geology, Kyungpook National University, Daegu, South Korea
2Rural Research Institute, Korea Rural Community Corporation, Ansan, South Korea
1경북대학교 지질학과 2한국농어촌공사 농어촌연구원
Received January 8, 2021; Revised January 26, 2021; Accepted January 26, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Abstract
In this study, a method has been proposed to improve the performance of hydraulic property estimation model developed by Jeong et al. (2020). In their study, low-dimensional features of the annual groundwater level (GWL) fluctuation patterns extracted based on a Denoising autoencoder (DAE) was used to develop a regression model for predicting hydraulic properties of an aquifer. However, low-dimensional features of the DAE are highly dependent on the precipitation pattern even if the GWL is monitored at the same location, causing uncertainty in hydraulic property estimation of the regression model. To solve the above problem, a process for generating the GWL fluctuation pattern for conditioning the precipitation is proposed based on a conditional variational autoencoder (CVAE). The CVAE trains a statistical relationship between GWL fluctuation and precipitation pattern. The actual GWL and precipitation data monitored on a total of 71 monitoring stations over 10 years in South Korea was applied to validate the effect of using CVAE. As a result, the trained CVAE model reasonably generated GWL fluctuation pattern with the conditioning of various precipitation patterns for all the monitoring locations. Based on the trained CVAE model, the low-dimensional features of the GWL fluctuation pattern without interference of different precipitation patterns were extracted for all monitoring stations, and they were compared to the features extracted based on the DAE. Consequently, it can be confirmed that the statistical consistency of the features extracted using CVAE is improved compared to DAE. Thus, we conclude that the proposed method may be useful in extracting a more accurate feature of GWL fluctuation pattern affected solely by hydraulic characteristics of the aquifer, which would be followed by the improved performance of the previously developed regression model.
Keywords : hydraulic property estimation, groundwater level fluctuation pattern, precipitation pattern, generative model, conditional variational autoencoder

 

February 2021, 54 (1)