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Open AccessArticle

Sensitivity Analysis on the Rising Relation between Short-Term Rainfall and Groundwater Table Adjacent to an Artificial Recharge Lake

1
Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
2
Department of Irrigation and Engineering, Council of Agriculture Executive Yuan, Taipei 10014, Taiwan
*
Author to whom correspondence should be addressed.
Water 2019, 11(8), 1704; https://doi.org/10.3390/w11081704
Received: 15 July 2019 / Revised: 13 August 2019 / Accepted: 14 August 2019 / Published: 16 August 2019
(This article belongs to the Special Issue Rainfall Infiltration Modeling)
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Abstract

This study aimed to determine the highly sensitive variables for a groundwater simulation model adjacent to an artificial recharge lake (ARL) using short-term rainfall events. The model was established using an artificial neural network (ANN) with rainfall events. Normalized rainfall, rainfall intensity, and groundwater data were selected as model variables. The coefficient of determination (R2) was used for model performance assessment. Finally, a sensitivity analysis (SA) was conducted to evaluate the importance of each model input. The study results indicated that the R2 of the ANN model ranged between 0.759 and 0.914. The SA showed that the rainfall was more sensitive than rainfall intensity in the study area. Based on the SA results and relevant geological characteristics, it was observed that the rainfall of past 1-day, past 2-day, and past 3-day responded faster than the other variables to the wells near the river and the ARL. In addition, the past 2-day rainfall was highly sensitive to the groundwater table; this may be due to the fact that the well screen location was above sea level as observed in Wells 1, 2, and 6. The results indicate that the groundwater table variation is response-related to the distance from the wells to the river and the ARL, and the rainfall time-lag. This SA study is helpful to researchers wishing to study related ARL efficiency issues. View Full-Text
Keywords: groundwater recharge; infiltration; Lin-Bien River; rainfall intensity; artificial neural network; ANN groundwater recharge; infiltration; Lin-Bien River; rainfall intensity; artificial neural network; ANN
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Hsieh, S.-H.; Liu, L.-W.; Chung, W.-G.; Wang, Y.-M. Sensitivity Analysis on the Rising Relation between Short-Term Rainfall and Groundwater Table Adjacent to an Artificial Recharge Lake. Water 2019, 11, 1704.

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