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

Lee, Sanghoon; Lee, Kang-Kun; Yoon, Heesung

Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors | Utilisation des modèles de réseaux neuronaux artificiels pour la prévision du niveau des eaux souterraines et l’estimation des impacts relatifs des facteurs influents Uso de modelos de redes neuronales artificiales para el pronóstico del nivel del agua subterránea y evaluación de los impactos relativos de los factores influyentes 利用人工神经网络模型预测地下水位和评价影响因素的相关影响 Utilizando redes neurais artificiais para previsão de níveis de águas subterrâneas e avaliação dos impactos relativos dos fatores influenciadores

2019

Lee, Sanghoon | Lee, Kang-Kun | Yoon, Heesung

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.

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