Water Quality Prediction Method Based on IGRA and LSTM

Abstract

Water quality prediction has great significance for water environment protection. A water quality prediction method based on the Improved Grey Relational Analysis (IGRA) algorithm and a Long-Short Term Memory (LSTM) neural network is proposed in this paper. Firstly, considering the multivariate correlation of water quality information, IGRA, in terms of similarity and proximity, is proposed to make feature selection for water quality information. Secondly, considering the time sequence of water quality information, the water quality prediction model based on LSTM, whose inputs are the features obtained by IGRA, is established. Finally, the proposed method is applied in two actual water quality datasets: Tai Lake and Victoria Bay. Experimental results demonstrate that the proposed method can take full advantage of the multivariate correlations and time sequence of water quality information to achieve better performance on water quality prediction compared with the single feature or non-sequential prediction methods.