Journal
WATER
Volume 14, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/w14030329
Keywords
rough set; NARX neural network; water consumption; prediction; attribute reduction
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This paper proposes a nonlinear autoregressive model with an exogenous input neural network model based on rough set theory for predicting water consumption. The experimental results show that the proposed model performs better in terms of prediction accuracy.
Scientific prediction of water consumption is beneficial for the management of water resources. In practice, many factors affect water consumption, and the various impact mechanisms are complex and uncertain. Meanwhile, the water consumption time series has a nonlinear dynamic feature. Therefore, this paper proposes a nonlinear autoregressive model with an exogenous input (NARX) neural network model based on rough set (RS) theory. First, the RS theory was used to analyze the importance of each attribute in water consumption. Then, the main influencing factor was selected as the input of the NARX neural network model, which was applied to predict water consumption. The proposed model is proved to give better results of a single NARX model and a back propagation neural network. The experimental results indicate that the proposed model has higher prediction accuracy in terms of the mean absolute error, mean absolute percentage error and root mean square error.
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