On the capability of preprocessing techniques for suspended sediment load prediction using artificial intelligence methods

With growing interest in global environmental change, it is crucial to have a reliable model to accurately predict the suspended sediment load (SSL). This study investigates the efficiency and capability of two time-series data decomposition algorithms, namely empirical mode decomposition (EMD) and ensemble EMD (EEMD), to address the complexity and non-stationary behaviour of the monthly SSL time series data. These algorithms were coupled with three artificial intelligence models - gene expression programming (GEP), extreme learning machine (ELM), and extreme gradient boosting (XGBoost) - to predict SSL at two stations in Tajan Basin, northern Iran. The results indicate that EEMD, with coefficient of determination values higher than 0.9, performed better than EMD when coupled with XGBoost as the ensemble model. To verify the the predicted SSL data, Monte Carlo simulation was used. The results confirmed the lower uncertainty of the EEMD-XGBoost model in predicting SSL at a monthly scale.