Ecology and Resilient Infrastructure

Korean Society of Ecology and Infrastructure Engineering (응용생태공학회)
권호 표지
DOI QR코드

DOI QR Code

The Effect of Seasonal Input on Predicting Groundwater Level Using Artificial Neural Network

인공신경망을 이용한 지하수위 예측과 계절효과 반영을 위한 입력치의 영향

  • Kim, Incheol (School of Civil and Environmental Engineering, Yonsei University) ;
  • Lee, Junhwan (School of Civil and Environmental Engineering, Yonsei University)
  • 김인철 (연세대학교 사회환경시스템공학과) ;
  • 이준환 (연세대학교 사회환경시스템공학과)
  • Received : 2018.09.11
  • Accepted : 2018.09.18
  • Published : 2018.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Artificial neural network (ANN) is a powerful model to predict time series data and have been frequently adopted to predict groundwater level (GWL). Many researchers have also tried to improve the performance of ANN prediction for GWL in many ways. Dummies are usually used in ANN as input to reflect the seasonal effect on predicted results, which is necessary for improving the predicting performance of ANN. In this study, the effect of Dummy on the prediction performance was analyzed qualitatively and quantitatively using several graphical methods, correlation coefficient and performance index. It was observed that results predicted using dummies for ANN model indicated worse performance than those without dummies.

인공신경망 (Artificial neural network, ANN)은 간편히 시계열 데이터를 예측할 수 있는 모델 중에 하나로 지하수위를 예측하는데 빈번히 사용되었으며, 많은 연구자들이 ANN으로 지하수위 예측에 있어서 높은 예측 신뢰성을 얻기 위하여 노력해 왔다. 본 연구에서는 ANN를 이용한 지하수위 예측 시 계절 효과를 반영하기 위한 input으로 사용되는 Dummy가 지하수위 예측 결과에 미치는 영향에 대하여 분석하였다. 정성적 및 정량적인 분석을 위하여 도해법과 상관계수, 에러 지수를 이용하였다. 분석결과 하천변 도심지역에서는 ANN의 input으로 사용된 Dummy가 오히려 예측 신뢰성을 떨어뜨리는 결과를 보였다.

Keywords

References

  1. Ausilio, E. and Conte, E. 2005. Influence of groundwater on the bearing capacity of shallow foundations. Canadian Geotechnical Journal 42: 663-672. https://doi.org/10.1139/t04-084
  2. Daliakopoulos, I.N., Coulibaly, P., and Tsanis I.K. 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology 309: 229-240. https://doi.org/10.1016/j.jhydrol.2004.12.001
  3. Dawson, C.W. and Wilby, R.L. 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography 25(1): 80-108. https://doi.org/10.1177/030913330102500104
  4. Geotechnical Information Database System (GIDS), Ground Investigation Tap. http://surveycp.seoul.go.kr
  5. Kim, I. and Lee, J. 2018a. Influencing factor analysis on groundwater level fluctuation near river. Ecology and Resilient Infrastructure 5(2): 72-81 (in Korean) https://doi.org/10.17820/ERI.2018.5.2.072
  6. Kim, I. and Lee, J. 2018b. Prediction model for spatial and temporal variation of groundwater level based on river stage." Journal of Hydrologic Engineering 23(6): 0601 8002-1-8.
  7. Makridakis, S., Wheelwright, S.C., Hyndman, R.J. 2008. Forecasting methods and applications, 3rd edn. Wiley, Singapore, 656 pp.
  8. Nourani, V. and Mousavi, S. 2016. Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method. Journal of Hydrology 536: 10-25. https://doi.org/10.1016/j.jhydrol.2016.02.030
  9. Park, D., Kim, I., Kim, G., and Lee, J. (2017). "Groundwater effect factors for the load-carrying behavior of footings from hydraulic chamber load tests." Geotechnical Testing Journal 30(3): 440-451.
  10. Sahoo, S. and Jha, M. K. 2013. Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeology Journal 21: 1865-1887. https://doi.org/10.1007/s10040-013-1029-5
  11. Seo, Y., Kim, S., Kisi, O., and Singh, V.P. 2015. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. Journal of Hydrology 520: 224-243. https://doi.org/10.1016/j.jhydrol.2014.11.050
  12. Shahriar, M. A., Sivakugan, N., Das, B. M., Urquhart, A., and Tapiolas, M. 2014. Water table correction factors for settlements of shallow foundations in granular soils. International Journal of Geomechanics, 10.1061/(ASCE)GM.1943-5622.0000391, 1-7.
  13. Trichakis, I.C., Nikolos, I.K., and Karatzas, G.P. 2011. Artificial neural network (ANN) based modeling for Karstic groundwater level simulation. Water Resources Management 25: 1143-1152. https://doi.org/10.1007/s11269-010-9628-6
  14. Yasuhara, K., Murakami, S., Mimura, N., Komine, H., and Recio, J. 2007. Influence of global warming on coastal infrastructural instability. Sustainability Science 2: 13-25. https://doi.org/10.1007/s11625-006-0015-4