Analysis of Groundwater Level Prediction Performance with Influencing Factors by Artificial Neural Network

Incheol Kim; Jaehwan Lee; Junghwan Kim; Hyoungkyu Lee; Junhwan Lee

doi:10.7843/kgs.2021.37.5.19

All Issue

2021 Vol.37, Issue 5 Preview Page Next Page

Research Article

Analysis of Groundwater Level Prediction Performance with Influencing Factors by Artificial Neural Network 지하수위 영향인자에 따른 인공신경망 기반의 지하수위 예측 성능 분석

31 May 2021. pp. 19-31

PDF XML

Abstract

Groundwater level (GWL) causes the stress state within soil and affects the bearing capacity and the settlement of foundation. In this study, the analyses of influencing factors on GWL fluctuation were performed. From the results, river stage and moving average of precipitation were main influence components for urban near large river and rural areas, respectively. In addition, the prediction performance of GWL using artificial neural network (ANN) was conducted with respect to the influence components. As a result, the effect of main component was significant on the prediction performance of GWL.

Keywords

Artificial neural network

Groundwater level

Influencing factors

Moving average

Prediction performance

지하수위 변동은 지반의 응력 상태에 변화를 일으켜 기초구조물의 지지력 및 침하에 직･간접적인 영향을 미칠 수 있다. 본 연구에서는 연구 대상지역을 선정하여 지하수위 영향인자 분석을 수행하였다. 그 결과 대상지역에 따라 지하수위에 미치는 영향인자들이 각각 달랐으며, 규모가 큰 하천변에 위치한 도심지역의 경우 하천수위가 지하수위 변동에 영향을 미치는 주요 인자였으며, 지표면 포장율이 낮은 도외지역의 경우는 선행강우를 고려하기 위해 도입된 강우이동평균이 주요 인자였다. 또한, 여러 입력 인자 조합을 고려하여 인공신경망을 통한 지하수위를 예측을 수행하였다. 분석결과 주요 지하수위 영향인자가 지하수위 예측 성능에 미치는 영향이 큰 것으로 나타났다. 결과적으로, 인공신경망을 이용하여 지하수위를 예측할때, 적절한 지하수위 영향인자 평가가 수행되어야 하며 이를 예측에 적용할 필요가 있는 것을 나타낸다.

References

Ausilio, E. and Conte, E. (2005), "Influence of Groundwater on the Bearing Capacity of Shallow Foundations", Canadian Geotechnical Journal, Vol.42, pp.663-672. 10.1139/t04-084

Batelaan, O., De Smedt, F., and Triest, L. (2003), "Regional Groundwater Discharge: Phreatophyte Mapping, Groundwater Modeling and Impact Analysis of Land-use Change", Journal of Hydrology, Vol.275, pp.86-108. 10.1016/S0022-1694(03)00018-0

Coppola, E. A., Rana, A. J., Poulton, M. M., Szidarovszky, F., and Uhl, V. W. (2005), "A Neural Network Model for Predicting Aquifer Water Level Elevations", Groundwater, Vol.43, No.2, pp.231-241. 10.1111/j.1745-6584.2005.0003.x15819944

Daliakopoulos, I. N., Coulibaly, P., and Tsanis, I. K. (2005), "Groundwater Level Forecasting Using Artificial Neural Networks", Journal of Hydrology, Vol.309, pp.229-240. 10.1016/j.jhydrol.2004.12.001

Dawson, C. W. and Wilhy, R. L. (2001), "Hydrological Modelling Using Artificial Neural Networks", Progress in Physical Geography, Vol.25, No.1, pp.80-108. 10.1177/030913330102500104

Gerken, W. C., Purvis, L. K., and Butera, R. J. (2006), "Genetic Algorithm for Optimization and Specification of a Neuron Model", Neuroconputing, Vol.69, pp.1039-1042. 10.1016/j.neucom.2005.12.041

Guttman, N. B. (1999), "Accepting the Standardized Precipitation Index: A Calculation Algorithm", Journal of the American Water Resources Association, Vol.35, No.2, pp.311-322. 10.1111/j.1752-1688.1999.tb03592.x

Hoque, M., Hoque, M., and Ahmed, K. (2007), "Declining Groundwater Level and Aquifer Dewatering in Dhaka Metropolitan Area, Bangladesh: Causes and Quantification", Hydrogeology Journal, Vol.15, pp.1523-1534. 10.1007/s10040-007-0226-5

Kim, I., Park, D., Kyung, D., Kim, G., Kim, S., and Lee, J. (2016), "Comparative Influences of Precipitation and River Stage on Groundwater Levels in Near-river Areas", Sustainability, Vol.8, No.1, pp.1-16. 10.3390/su8010001

Kim, I. and Lee, J. (2018a), "Prediction Model for Spatial and Temporal Variation of Groundwater Level based on River Stage", Journal of Hydrologic Engineering, Vol.23, No.67, DOI: 10.1061/(ASCE)HE.1943-5584.0001658, 1-8. 10.1061/(ASCE)HE.1943-5584.0001658

Kim, I. and Lee, J. (2018b), "The Effect of Seasonal Input on Predicting Groundwater Level Using Artificial Neural Network", Ecology and Resilient Infrastructure, Vol.5, No.3, pp.125-133.

Kim, I. and Lee, J. (2018c), "Influencing Factor Analysis on Groundwater Level Fluctuation Near River", Ecology and Resilient Infrastructure, Vol.5, No.2, pp.72-81.

Kim, I. and Lee, J. (2019), "Proposed Correlation Model for Groundwater Level Prediction based on River Stage Considering Changes in Hydrological and Geological Conditions", Journal of Hydrologic Engineering, Vol.24, No.10, DOI: 10.1061/(ASCE)HE.1943-5584.0001849, 1-14. 10.1061/(ASCE)HE.1943-5584.0001849

Nourani, V. and Mousavi, S. (2016), "Spatiotemporal Groundwater Level Modeling Using Hybrid Artificial Intelligence-meshless Method", Journal of Hydrology, Vol.536, pp.10-25. 10.1016/j.jhydrol.2016.02.030

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, Vol.30, No.3, pp.440-451. 10.1520/GTJ20160078

Park, D., Kim, I. Kim, G., and Lee, J. (2019), "Effect of Groundwater Fluctuation on Load Carrying Performance of Shallow Foundation", Geomechanics and Engineering, Vol.18, No.6, pp.575-584.

Rajaee, T., Ebrahimi, H., and Nourani, V. (2019), "A Review of the Artificial Intelligence Methods in Groundwater Level Modeling", Journal of Hydrology, Vol.572, pp.336-351. 10.1016/j.jhydrol.2018.12.037

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 J. of Geomechanics, 10.1061/(ASCE)GM.1943-5622.0000391.

Serrano, S. E. and Workman, S. R. (1998), "Modeling Transient Stream/aquifer Interaction with the Non-linear Boussinesq Equation and its Analytical Solution", Journal of Hydrology, Vol.206, pp.145-255. 10.1016/S0022-1694(98)00111-5

Sahoo, S. and Jha, M. K. (2013), "Groundwater-level Prediction Using Multiple Linear Regression and Artificial Neural Network Techniques: A Comparative Assessment", Hydrogeology Journal, Vol.21, pp.1865-1887. 10.1007/s10040-013-1029-5

Shamsudduha, M., Chandler, R. E., Taylor, R. G., and Ahmed, K. M. (2009), "Resent Trends in Groundwater Levels in a Highly Seasonal Hydrological System: The Ganges-Brahmaputra-Meghna Delta", Hydrology and Earth System Sciences, Vol.13, pp.2373-2385. 10.5194/hess-13-2373-2009

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, Vol.25, pp.1143-1152. 10.1007/s11269-010-9628-6

Uddameri, V. (2007), "Using Statistical and Artificial Neural Network Models to Forecast Potentiometric Levels at a Deep Well in South Texas", Environmental Geology, Vol.51, pp.885-895. 10.1007/s00254-006-0452-5

Wen, X., Feng, Q., Yu, H., Wu, J., Si, J., Chang, Z., and Xi., C. (2015), "Wavelet and Adaptive Neuro-fuzzy Inference System Conjunction Model for Groundwater Level Predicting in a Coastal Aquifer", Neural Computing and Applications, Vol.26, pp.1203-1215. 10.1007/s00521-014-1794-7

Wilhite, D. A. and Glants, M. H. (1985), "Understanding the Drought Phenomenon: The Role of Definitions", Water International, Vol.10, pp.110-120. 10.1080/02508068508686328

Yang, J. S. and Kim, N. K. (2011), "The Correlation between the Moving Average of Precipitation and Groundwater Level in Korea", Korean Society of Civil Engineers, Vol.31, pp.265-276.

Yasuhara, K., Murakami, S. Mimura, N. Komine, H., and Recio, J. (2007), "Influence of Global Warming on Coastal Infrastructural Instability", Sustainability Science, 2, pp.13-25. 10.1007/s11625-006-0015-4

Information

Publisher :The Korean Geotechnical Society
Publisher(Ko) :한국지반공학회
Journal Title :Journal of the Korean Geotechnical Society
Journal Title(Ko) :한국지반공학회 논문집
Volume : 37
No :5
Pages :19-31
Received Date : 2020-12-01
Revised Date : 2021-03-23
Accepted Date : 2021-04-08
DOI :https://doi.org/10.7843/kgs.2021.37.5.19

Journal of the Korean Geotechnical Society ISSN:1229-2427(Print) 2288-646X(Online) 한국지반공학회 논문집

All Issue