Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

Abstract

In this study, ensemble models using the Bates-Granger approach and least square method are developed to combine forecasts of multi-wavelet artificial neural network (ANN) models. Originally, this study is aimed to investigate the proposed models for forecasting of chlorophyll a concentration. However, the modeling procedure was repeated for water salinity forecasting to evaluate the generality of the approach. The ensemble models are employed for forecasting purposes in Hilo Bay, Hawaii. Moreover, the efficacy of the forecasting models for up to three days in advance is investigated. To predict chlorophyll a and salinity with different lead, the previous daily time series up to three lags are decomposed via different wavelet functions to be applied as input parameters of the models. Further, outputs of the different wavelet-ANN models are combined using the least square boosting ensemble and Bates-Granger techniques to achieve more accurate and more reliable forecasts. To examine the efficiency and reliability of the proposed models for different lead times, uncertainty analysis is conducted for the best single wavelet-ANN and ensemble models as well. The results indicate that accurate forecasts of water temperature and salinity up to three days ahead can be achieved using the ensemble models. Increasing the time horizon, the reliability and accuracy of the models decrease. Ensemble models are found to be superior to the best single models for both forecasting variables and for all the three lead times. The results of this study are promising with respect to multi-step forecasting of water quality parameters such as chlorophyll a and salinity, important indicators of ecosystem status in coastal and ocean regions.