A water quality assessment method based on sparse autoencoder

Water quality assessment is very important for monitoring water sources and main canal, which is beneficial to offer strategies for the management of water quality and environment. This paper proposes a water quality assessment method based on a sparse autoencoder network. In the proposed approach, a representation model is firstly learned via a sparse autoencoder trained by unlabeled water monitoring data acquired from DanJiangKou reservoir, then a softmax classifier is trained using a small set of labeled classification data based on the China Surface Water Environmental Quality Standard (GB3838-2002) expressed by the sparse autoencoder. The combined model is finally used to evaluate the water quality. Experimental results show that the proposed method in this paper is of high robustness and accuracy of water quality assessment, and has a good prospect of practical applications.