Shallow groundwater is generally of great interest to the community due to its easy availability. However, it is very sensitive to external stimulus. In this paper, shallow groundwater quality is assessed and classified with improved Nemerow pollution index, multi-layer perceptron artificial neural network (MLP-ANN) optimized with a back-propagation algorithm and wavelet neural network (WNN) methods in a coastal aquifer, Fujian Province, South China. The data used in three models were collected during the pre-monsoon over the period 2004–2011. The eight parameters, total dissolved solids, total hardness, chemical oxygen demand, chloride, sulphate, nitrate, nitrite and fluorides, were selected to characterize groundwater quality classification based on the National Quality Standard for Groundwater (GB/T 14848-93). The results of MLP-ANN and WNN are interpreted by mean absolute error, root mean square error and R2 (determination coefficient) criteria. The results obtained from three methods demonstrate that WNN has a higher accuracy compared with the other two methods. The study reveals that these methods are efficient tools for assessing groundwater quality.
Shallow groundwater quality assessment: use of the improved Nemerow pollution index, wavelet transform and neural networks
Q. Yang, J. Zhang, Z. Hou, X. Lei, W. Tai, W. Chen, T. Chen; Shallow groundwater quality assessment: use of the improved Nemerow pollution index, wavelet transform and neural networks. Journal of Hydroinformatics 13 September 2017; 19 (5): 784–794. doi: https://doi.org/10.2166/hydro.2017.224
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