Missing Value Imputation and Prediction of River Water Quality Based on GRU–Autoencoder with Input-decay

With the development of human beings, the pollution of freshwater resources occurs very frequently, which directly leads to the reduction of available water sources and the increase of maintenance costs. In order to manage and protect water resources better, a large number of sensors are used to monitor water quality. However, the problem of missing data has a negative impact on our data analysis work. In this paper, we propose a new missing value imputation model which based on GRU autoencoder with input-decay(GRUD-AE). The prediction module is added to the output of the autoencoder to realize the short-term prediction. The model is trained with datasets which contain missing value. The input-decay module is applied to learning the time correlation of sequences which could help improve imputation accuracy. Experiments show that our model performs better than the traditional missing value imputation and prediction methods.