关键词: 神经网络, 水质预测, 交叉验证

Abstract: In a previous study, using artificial neural network to predict the water quality indicators has made some progress. The application of cross-validation, based on the artificial network, is aimed to obtain a better result. This paper establishes the water quality prediction model based on the real-world data about one of the monitor stations on Lishui. It chooses six water indicators include Total Phosphorus（TP）, Total Nitrogen（TN）, Dissolved Oxygen（DO）. It uses weighted k-fold cross-validation method to build a collection of neural networks, based on the Levenberg-Marquardt optimization algorithm, under three different combination methods：mean, median, and weighted-based. Several experiments are held, using real-world time series with different water quality index. Overall, the proposed approach achieves competitive results when compared with the simpler0-fold ANN. At the same time, the TN and DO get the best result compared with other index.

Key words: artificial network, water quality prediction, cross-validation