An recurrent neural network application to forecasting the quality of water diversion in the water source of Lake Taihu

Abstract

This paper describes the training, validation and application of recurrent neural network (RNN) models to computing the total nitrogen (TN), total phosphorus (TP) and dissolved oxygen (DO) at three different sites in Gonghu Bay of Lake Taihu during the period of water diversion. The input parameters of Elman's RNN were selected by means of the principal component analysis (PCA). Sequentially, the conceptual models for Elman's RNN of different simulated parameters were established and the Elman models were trained and validated on daily data set. The values of TN, TP and DO computed by the models were closely related to their respective values measured at the three sites. The results show that the PCA can efficiently ascertain appropriate input parameters for Elman's RNN and the Elman's RNN can precisely compute and forecast the water quality parameters during the period of water diversion.