Estuary salinity prediction using a coupled GA-SVM model: a case study of the Min River Estuary, China

Abstract

Estuary salinity predictions can help to improve water safety in coastal areas. Coupled genetic algorithm-support vector machine (GA-SVM) models, which adopt a GA to optimize the SVM parameters, have been successfully applied in some research fields. In light of previous research findings, an application of a GA-SVM model for tidal estuary salinity prediction is proposed in this paper. The corresponding model is developed to predict the salinity of the Min River Estuary (MRE). By conducting an analysis of the time series of daily salinity and the results of simulation experiments, the high-tide level, runoff and previous salinity are considered as the major factors that influence salinity variation. The prediction accuracy of the GA-SVM model is satisfactory, with coefficient of determination (R2) of 0.85, Nash–Sutcliffe efficiency of 0.84 and root mean square error of 119 (μS/cm). The proposed model performs significantly better than the traditional SVM model in terms of prediction accuracy and computing time. It can be concluded that the proposed model can successfully predict the salinity of MRE based on the high-tide level, runoff and previous salinity.