The Journal of Engineering Geology (지질공학)

The Korean Society of Engineering Geology (대한지질공학회)
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Determination of the Groundwater Yield of horizontal wells using an artificial neural network model incorporating riverside groundwater level data

배후지 지하수위를 고려한 인공신경망 기반의 수평정별 취수량 결정 기법

  • Received : 2018.09.11
  • Accepted : 2018.11.17
  • Published : 2018.12.31
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Abstract

Recently, concern has arisen regarding the lowering of groundwater levels in the hinterland caused by the development of high-capacity radial collector wells in riverbank filtration areas. In this study, groundwater levels are estimated using Modflow software in relation to the water volume pumped by the radial collector well in Anseongcheon Stream. Using the water volume data, an artificial neural network (ANN) model is developed to determine the amount of water that can be withdrawn while minimizing the reduction of groundwater level. We estimate that increasing the pumping rate of the horizontal well HW-6, which is drilled parallel to the stream direction, is necessary to minimize the reduction of groundwater levels in wells OW-7 and OB-11. We also note that the number of input data and the classification of training and test data affect the results of the ANN model. This type of approach, which supplements ANN modeling with observed data, should contribute to the future groundwater management of hinterland areas.

최근들어 방사형 집수정 방식의 대용량 강변여과수 개발에 따른 배후지의 지하수위 강하에 대한 우려가 존재하고 있다. 본 연구에서는 안성천의 방사형 집수정을 대상으로 Modflow를 활용하여 수평정의 취수량에 따른 배후지의 수위 강하를 예측하였으며, 이 데이터를 기반으로 배후지 수위 강하가 최소가 되는 수평정별 취수량을 결정하는 다층퍼셉트론 기반의 인공신경망 모델을 개발하였다. 하천 방향으로 굴착된 수평정 HW-6의 취수량을 높이는 것이 OW-7 및 OB-11 관측정의 지하수위를 높게 유지하는데 필요한 것으로 평가되었다. 또한, 모델 입력 자료의 수 및 훈련과 검증 자료의 분류는 인공신경망 모델 결과에 영향을 미치므로 유의하여야 한다. 향후 현장의 실제 운영 자료와 수치모델의 비교를 통하여 인공신경망 모델을 보완한다면 배후지의 지하수 관리에 기여할 것으로 본다.

Keywords

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Fig. 1. (a) Location of the study area and (b) location of the radial collector well and groundwater-monitoring wells (adapted from Kim et al., 2016)

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Fig. 2. Design of the numerical model and comparison of actual and estimated water levels.

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Fig. 3. Multilayer perceptron concept of an artificial neural network with a hidden layer.

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Fig. 4. Scatterplots of original versus estimated groundwater levels at the two monitoring wells.

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Fig. 5. Simulation results for 52 cases of the pumping rate of a collector well for the two monitoring wells.

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Fig. 6. Scatterplots of horizontal well yields (y-axis) versus groundwater levels (x-axis).

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Fig. 7. Estimations of groundwater level at the monitoring wells for three cases: (1) Case 1, nine data points; (2) Case 2, eight data points; and (3) Case 3, five data points.

Table 1. Specifications of the radial collector well and hydraulic features of the aquifer

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Table 2. Simulation results for collector well pumping and the numerically estimated groundwater levels for the monitored wells

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Table 2. Simulation results for collector well pumping and the numerically estimated groundwater levels for the monitored wells (Continued)

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Table 3. Representative cases of groundwater level estimation using the ANN model

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Table 4. Characteristics and structure of the ANN models for the three cases

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Table 5. Statistics of estimated groundwater levels at two monitored wells

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