The Journal of Engineering Geology (지질공학)

The Korean Society of Engineering Geology (대한지질공학회)
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Site Selection Method by AHP-based Artificial Neural Network Model for Groundwater Artificial Recharge

AHP 기반의 인공신경망 모델을 활용한 지하수 인공함양 후보지 선정 방안

  • Kim, Gyoo-Bum (Department of Construction Safety and Disaster Prevention, Daejeon University) ;
  • Choi, Myoung-Rak (Department of Construction Safety and Disaster Prevention, Daejeon University) ;
  • Seo, Min-Ho (Industry-Academic Cooperation Foundation, Daejeon University)
  • 김규범 (대전대학교 건설안전방재공학과) ;
  • 최명락 (대전대학교 건설안전방재공학과, 대학원) ;
  • 서민호 (대전대학교 산학협력단)
  • Received : 2018.11.12
  • Accepted : 2018.12.24
  • Published : 2018.12.31
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Abstract

Local drought in South Korea has recently increased interest in the efficient use of groundwater and then induces a growing need to introduce artificial recharge of groundwater that stores water in sedimentary layer. In order to evaluate the potential artificial recharge sites in the alluvial basins in Chungcheongnamdo province, an AHP (Analytical hierarchy process) model consisting of three primary and seven secondary factors was developed in this study. In the AHP model, adding candidate sites changes final evaluation score through a mathematical calculation process. By contrast ANN (Artificial neural network) model always provides an unchanged score for each candidate area. Therefore, the score can be used as a selection criterion for artificial recharge sites. It is concluded that the possibility of artificial recharge is relatively low if the score of the ANN model is less than about 1.5. Further studies and field surveys on the other regions in Korea will lead to draw out a more applicable ANN model.

최근 우리나라에서 발생되는 국지적 가뭄은 지하수의 효율적 활용에 대한 관심을 증대시키고 있으며, 잉여의 물을 지층 내에 저장하는 지하수 인공함양 기술 도입의 필요성이 대두되고 있다. 본 연구에서는 충청남도내 퇴적 분지의 지하수 인공함양 대상지로의 가능성을 평가하기 위하여 1차 인자 3개, 2차 인자 7개로 구성된 AHP 모델을 개발하였으며, 10개 후보지에 적용한 결과를 토대로 인공신경망 모델을 구축하였다. AHP 모델은 후보지가 추가될 경우 수학적인 연산 과정에 의하여 최종 평가점수가 변하게 되나, 인공신경망 모델은 후보지별 고정적인 최종평가 점수를 제시하게 되어 인공함양 적지 선정 기준으로 사용할 수 있다. 충청남도 지역의 연구 결과, 인공신경망 모델의 최종 평가점수가 약 1.5점 이하인 경우에는 인공함양 후보지로서의 가능성이 낮은 것으로 평가되었다. 향후 타 지역에 대한 추가 연구 및 현장 조사를 통해 다양한 자료 군을 확보한다면 보다 보편적으로 적용할 수 있는 인공신경망 모델 도출이 가능할 것이다.

Keywords

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Fig. 1. Flow diagram of a generalized model development by using AHP and ANN.

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Fig. 2. Hierarchy structure of primary and secondary evaluation factors in AHP model.

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Fig. 3. Location of the target sites for developing an AHP model.

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Fig. 4. Relationship between original and estimated factor scores for three cases of ANN model.

Table 1. Basic factors related with artificial recharge and selection of main factors

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Table 2. Example of a relative importance determination of the primary evaluation factors by using a pairwise comparison.

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Table 3. Calculation of average evaluation score for secondary evaluation factors

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Table 4. Relative importance for secondary evaluation factors

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Table 5. Input data and standardized data for the AHP analysis

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Table 6. Final evaluation scores for 10 sites by using the AHP model

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Table 7. Three cases of training and test data selection (◯: training data, ✕: test data) and factor score estimations by ANN models

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Table 8. Possibility assessment of artificial recharge for additional eleven sites in Chungnam province by using the ANN model

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Table 9. Statistics for input variables and estimated factor score of all (21) sites by using the ANN model

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