Coupling wavelet transform and artificial neural network for forecasting estuarine salinity

By F. Zhou, Bingjun Liu, Kai Duan. 2020

56 citations recorded by the source corpus.

Abstract Accurate and reliable predictions of estuarine salinity can lead to more effective water resource management and can alleviate the adverse impacts of saltwater intrusion. Due to the nonlinear and nonstationary features in time series of estuarine salinity, this study is conducted to develop a hybrid model coupling the techniques of wavelet transform (WT) and artificial neural network (ANN) for forecasting estuarine salinity in the Pearl River Estuary, China. Two wavelet-based forecasting frameworks, the direct forecast (DF) framework (i.e., only explanatory variables are decomposed, leading to direct forecasting of the target variables) and multicomponent forecast (MF) framework (i.e., both explanatory and target variables are decomposed, and each target component is forecasted separately), were used to construct the WT-ANN models. The results reveal the superiority of hybrid WT-ANN models in estuarine salinity forecasting over traditional multiple linear regression (MLR) models and single ANN models, indicated by the Nash–Sutcliffe efficiency (NSE), correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and Kling–Gupta efficiency (KGE). Between the two wavelet-based forecasting paradigms, the MF framework outperformed the DF framework for better capturing the mutual dependence (i.e., mutual information, MI) between input and output variables. Additionally, the superiority of WT-ANN models in comparison with MLR and ANN models is increasingly prominent when the forecast lead time is extended from 1 to 3 days.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/c7fee73de6ca339530906626dbd83d661c313938

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In Electrical and Electronic Engineering

In Environmental Engineering

In Oceanography

Citations in the corpus

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression (2021, background): “This happens because WT decomposes complex time series by catching information on different levels, which results in a more effective structure for machine learning methods (Adamowski & Sun, 2010; Nourani et al., 2009; Zhou et al., 2020).”