A multiple process univariate model for the prediction of chlorophyll-a concentration in river systems

Abstract

The concentration of chlorophyll-a (Chl-a) in river systems is dependent on various hydrometric and biochemical factors, including an intricate array of corresponding growth and extinction mechanisms. This complex and interactive assortment of factors makes prediction of algal blooms difficult. This paper introduces an innovative time-series model structure that predicts Chl-a concentration in inland waters. To improve the prediction accuracy of existing models, we assume that the predicting variable is determined by multiple and independent drivers. An enhanced stochastic model, namely a multiple process univariate model (MPUM), is developed to address the impacts of the distinct mechanisms associated with each regulator (e.g., hydrometeorological factors and anthropogenic activities). Observations of the algae concentration at 16 weirs along four major river systems in South Korea are used to model Chl-a concentration. Comparisons between traditional models and the proposed method demonstrate the strengths of the MPUM, both in making predictions and in the parsimony of the model structure. The robustness of the developed model was further validated by modeling algae concentration before and after river-flow regulation procedures.