Use of NARX neural networks for Meteosat Second Generation SEVIRI very short-term cloud mask forecasting

Peronaci, Simone; Taravat, Alireza; Frate, Fabio Del; Oppelt, Natascha

doi:10.1080/2150704x.2016.1249296

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…The gated recurrent unit (GRU) neural network is a variant of the LSTM model, which solves the long dependency problem in RNNs, and the model predicts well [13]. As a special feedback dynamic neural network, a nonlinear autoregressive model with an exogenous input (NARX) neural network is a global feedback dynamic neural network [14]. It is based on the BPNN and introduces time-series delay blocks, so the NARX neural network has higher accuracy for predicting nonlinear dynamic time series [15].…”

Section: Introductionmentioning

confidence: 99%

A Water Consumption Forecasting Model by Using a Nonlinear Autoregressive Network with Exogenous Inputs Based on Rough Attributes

Zheng

Zhang

Xie³

et al. 2022

Water

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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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Section: Introductionmentioning

confidence: 99%