Statistical comparison between SARIMA and ANN’s performance for surface water quality time series prediction
- 27 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Environmental Science and Pollution Research
- Vol. 28 (25), 33531-33544
- https://doi.org/10.1007/s11356-021-13086-3
Abstract
The performance comparison studies of the autoregressive integrated moving average model (ARIMA) and the artificial neural network (ANN) were mostly carried out between the selected model structures through trial-and-error, strongly influenced by model structure uncertainty. This research aims to make up for this inadequacy. First, a surface water quality prediction case study including eight monitoring sites in China was introduced. Second, the ARIMA and ANN’s performance was compared statistically between 6912 Seasonal ARIMA (SARIMA) and 110,592 feedforward ANN with different model structures, based on the mean square error (MSE) distributions depicted by boxplots. In a statistical view, the ANN models obtained a significantly lower median value and a more concentrated distribution of validation MSEs, which indicated lighter overfitting and better generalization ability. Furthermore, the optimal SARIMA models’ performance is inferior to even the median of the ANN models in the case study. In contrast with the previous comparisons among selected models, the statistical comparison in this study shows lower uncertainty.Funding Information
- National Major Science and Technology Projects of China (2018ZX07208006)
- National Natural Science Foundation of China (51778451)
- 111 Project of Tongji University (B13017)
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