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http://dx.doi.org/10.9720/kseg.2018.4.583

Determination of the Groundwater Yield of horizontal wells using an artificial neural network model incorporating riverside groundwater level data  

Kim, Gyoo-Bum (Department of Construction Safety and Disaster Prevention, Daejeon University)
Oh, Dong-Hwan (Intellegeo Co. Ltd.)
Publication Information
The Journal of Engineering Geology / v.28, no.4, 2018 , pp. 583-592 More about this Journal
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water volume data, an artificial neural network (ANN) model is developed to determine the amount of water that can be withdrawn while minimizing the reduction of groundwater level.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.
Keywords
riverbank filtration; radial collector well; artificial neural network; groundwater level; numerical model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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