A universal calibrated model for the evaluation of surface water and groundwater quality: Model development and a case study in China. (1st November 2015)

• Record Type: Journal Article
• Authors: Yu, Chunxue; Yin, Xin'an; Li, Zuoyong; Yang, Zhifeng
• Is Part Of: Journal of environmental management. Volume 163(2015)
• Page Start: 20
• Page End: 27
• Publication Date: 2015-11-01
• Subjects: Neural network model -- Universal model -- Surface water -- Ground water -- Water quality evaluation; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2015.07.011
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Water quality evaluation is an important issue in environmental management. Various methods have been used to evaluate the quality of surface water and groundwater. However, all previous studies have used different evaluation models for surface water and groundwater, and the models must be recalibrated due to changes in monitoring indicators in each evaluation. Water quality managers would benefit from a universal and effective model based on a simple expression that would be suitable for all cases of surface water and groundwater, and which could therefore serve as a standard method for a region or country. To meet this requirement, we attempted to develop a universal calibrated model based on the radial basis function neural network. In the new model, the units and values of the evaluation indicators for surface water and groundwater are normalized simultaneously to make the data directly comparable. The model's training inputs comprise the normalized value in each of a water quality indicator's grades (e.g., the nitrate contents defined in a regulatory standard for grades I to V) for all evaluation indicators. The central vector of the Gaussian function is used as the average of the evaluation indicators' normalized standard values for the five grades. The final calibrated model is expressed as an equation rather than in a programming language, and is therefore easier to use. We used the model in a Chinese case study, and found that the model was feasible (it compared well with the results of other models) and simple to use for the evaluation of surface water and groundwater quality. Highlights: We build a universal calibrated model feasible to evaluate surface and ground water. A refined neural network is developed as a transitional means to achieve the model. The model is applied to a Chinese case study and compared well with other models. It can be used as a universal tool for region or country evaluation of water quality.