Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors

• Published in: Hydrogeology journal Vol. 27; no. 2; pp. 567 - 579
• Main Authors: Lee, Sanghoon, Lee, Kang-Kun, Yoon, Heesung
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-03-2019; Springer Nature B.V
• Subjects: Anthropogenic factors; Aquatic Pollution; Artificial neural networks; Correlation coefficient; Correlation coefficients; Cultivation; Distribution; Earth and Environmental Science; Earth Sciences; Exploitation; Forecasting; Geology; Geophysics/Geodesy; Greenhouses; Groundwater; Groundwater levels; Groundwater resources; Heat exchangers; Heat pumps; Human influences; Hydrogeology; Hydrology/Water Resources; Mathematical models; Neural networks; Nucleic acids; Predictions; Ribonucleic acid; Rivers; RNA; Spatial distribution; Variance; Variance analysis; Waste Water Technology; Water Management; Water Pollution Control; Water Quality/Water Pollution; Water resources; Groundwater management; South Korea; Groundwater recharge/water budget; Groundwater level forecasting; Artificial neural network
• ISSN: 1431-2174; 1435-0157
• DOI: 10.1007/s10040-018-1866-3

Change in groundwater level is predicted for a special site where transient natural factors affecting the groundwater level are mixed with very irregular anthropogenic influences. When there is not enough hydrogeological information about the area to be analyzed, an artificial neural network (ANN) is a powerful tool for groundwater level forecasting in highly irregular and uncertain groundwater systems. In this study, groundwater levels were predicted by using ANN models with input variables composed of one natural factor and two anthropogenic factors in Yangpyeong riverside area, South Korea. Complex and irregular change of the groundwater level was monitored due to the operation of a groundwater heat pump system and winter intensive pumping for water curtain cultivation (by which greenhouses are warmed). The prediction results showed good performance with root mean square errors of 3–6 cm when the average groundwater level is about 25.59 m, the correlation coefficient is >0.9 and the Nash–Sutcliffe efficiency is >0.75, indicating that the ANN models are well suited for assessing complex groundwater systems. Along with the prediction, an extraction method was devised to calculate contributions and relative impacts of the input variables in the time-series-based ANN models. As a result, it was proved that the river level dominantly affects the groundwater level fluctuation, and the contributions of each influencing factor were obtained reliably according to spatial distribution and temporal variance. This makes the scheme effective for managing and using groundwater resources with consideration of every crucial influencing factor of the groundwater level fluctuation.