Objective To build a water quality evaluation model based on back propagation (BP) neural network for better water hygiene management of the swimming pools in Shanghai.
Methods Based on the experts' proposed standards of water quality grading for swimming pools, random samples were selected. A BP neural network was adopted for training and modeling. The established model was then applied to rapid evaluation of water quality grade of swimming pools in Changning District, Shanghai.
Results With the help of BP neural network, the prediction accuracy of the water quality evaluation model was up to 95.2% for the training data. Among the water samples from swimming pools in Changning District in 2009, no severe water pollution was found and more than half (54.44%) of the water samples were at general grade. There were more lightly polluted samples from the community clubs than from the sports institutes and the star-rated hotels. The quality of samples taken from sports institutes was higher than those from the star-rated hotels and the community clubs.
Conclusion The accuracy of the BP neural network model is high for water quality evaluation. Except for the water sampled from the community clubs' swimming pools, the quality of water in swimming pools of Shanghai Changning District is at a general acceptable level.
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