An integrated autoregressive model for predicting water quality dynamics and its application in Yongding River

Abstract

It is important to predict evolution processes of water quality in order to effectively protect the aquatic eco-system. We proposed an integrated autoregressive (AR) prediction model based on Markov-switching (MS) theory and the improved crow search algorithm (ICSA), and the integrated model of ICSA-MSAR was used to predict temporal evolution of water quality changes. The results showed that the MS and ICSA provided an optimized method to determine the model parameters, and the integrated model with (s, p) values of (3, 5) had a better performance. Compared with the traditional AR model, the integrated ICSA-MSAR model significantly raised Akaike information criterion by 32.0%, Bayesian information criterion by 14.2%, Log-likelihood value by 40.5%, C index (C=AIC×BIC×LL) by 65.6%, and reduced the prediction relative error by 73.3%. The better performance mainly attributed to its offset capacity and convergence capacity. The integrated model can well predict the changes of CODMn in Yongding River located of North China during 2008–2018, and it is worth extending this model to predict the changes of other ecological indicators in water bodies.

Keywords

• Water quality model
• Autoregressive
• Markov-switching theory
• Improved Crow Search Algorithm
• Yingding River