A hybrid decomposition and Machine learning model for forecasting Chlorophyll-a and total nitrogen concentration in coastal waters

Abstract

Information regarding Chlorophyll-a (ChlA) and total nitrogen (TN) is critical for early warning of algal blooms. However, reliable models for accurate forecasting of the ChlA and TN are still lacking due to the optical complexity of coastal waters. To address this issue, we proposed a novel hybrid model named the CEEMDAN-VMD-XGBOOST-SARIMA (CVXS) model to forecast ChlA and TN concentrations. The model performance was validated at three hydrological monitoring stations in Hong Kong, China. Four independent models including extreme gradient boosting (XGBoost), support vector regression (SVR), deep learning (DL), and Seasonal autoregressive integrated moving average (SARIMA), and three hybrid models including complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-XGBoost, CEEMDAN-SVR, and CEEMDAN-DL were developed to compare their performance with the CVXS model. In addition, the physical mechanisms of the CVXS model were further explored through correlation analysis between the decomposed time series of water quality parameters. The result indicated that (1) the CVXS model had the best accuracy among all models for forecasting ChlA and TN, and all the NSEs remained above 0.97 at three hydrological monitoring stations. For forecasting ChlA, the performance of the eight models is ranked as CVXS > CEEMDAN-XGBoost > CEEMDAN-DL > CEEMDAN-SVR > XGBoost > DL > SARIMA > SVR. For forecasting TN, the performance of the eight models is ranked as CVXS > CEEMDAN-XGBoost > CEEMDAN-DL > CEEMDAN-SVR > XGBoost > SVR > SARIMA > DL; (2) the optimal forecasting time horizons of the CVXS model were one to two months; and (3) the variability of ChlA and TN concentrations induced by hydrologic factors has been inherently embedded in the decomposed time series data, thus providing the theoretical basis for the CVXS model forecasting water quality parameters. The results of this study are promising with respect to forecasting algal blooms and coastal water resource management.