Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling | Analyse de sensibilité des niveaux d’eau souterrains du Bassin de la Source Jinci (Chine) basée sur une modélisation par réseaux neuronaux artificiels Análisis de sensibilidad de niveles de agua subterránea en Jinci Spring Basin (China) basado en la modelación con redes neuronales artificiales 基于人工神经网络模型的中国晋祠泉流域地下水位敏感性分析 Análise de sensibilidade dos níveis piezométricos na Bacia da Nascente de Jinci (China), baseada em modelação por redes neuronais artificiais
2012
Li, Xian | Shu, Longcang | Liu, Lihong | Yin, Dan | Wen, Jinmei
Jinci Spring in Shanxi, north China, is a major local water source. It dried up in April 1994 due to groundwater overexploitation. The groundwater system is complex, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are statistical techniques to study parameter nonlinear relationships of groundwater systems. However, ANN models offer little explanatory insight into the mechanisms of prediction models. Sensitivity analysis can overcome this shortcoming. In this study, a back-propagation neural network model was built based on the relationship between groundwater level and its sensitivity factors in Jinci Spring Basin; these sensitivity factors included precipitation, river seepage, mining drainage, groundwater withdrawals and lateral discharge to the associated Quaternary aquifer. All the sensitivity factors were analyzed with Garson’s algorithm based on the connection weights of the neural network model. The concept of “sensitivity range” was proposed to describe the value range of the input variables to which the output variables are most sensitive. The sensitivity ranges were analyzed by a local sensitivity approach. The results showed that coal mining drainage is the most sensitive anthropogenic factor, having a large effect on groundwater level of the Jinci Spring Basin.
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