A novel water quality mechanism modeling and eutrophication risk assessment method of lakes and reservoirs

Water quality mechanism modeling and eutrophication analysis are important technical means for water pollution prevention and control of lake and reservoirs. However, the existing classical water quality mechanism models usually contain unknown parameters with empirical values range, which brings difficulty of predicting water quality changes of specific lakes and reservoirs to meet the accuracy requirements. Furthermore, the most existing water quality prediction methods output single-valued predictions of water quality indicators. These prediction results possess contingencies and uncertainties. Eutrophication analysis based on these results will bring errors, even become completely wrong. Therefore, combining the evolution mechanism of water quality, this paper proposes a fruit fly optimization algorithm (FFOA)-based water quality mechanism modeling method and studies a eutrophication risk assessment method of lakes and reservoirs based on the mechanism model. First, combining observed water quality data, unknown parameters of a water quality mechanism model are estimated by using FFOA. Then, Monte Carlo simulation is employed to predict the evolution of water quality, and the probability distribution functions (PDFs) of water quality indicators at predicted time indexes are obtained to achieve water quality prediction. Finally, to quantitatively assess the eutrophication status, a comprehensive eutrophication status index (CESI) is constructed. The PDF of CESI and the probabilities of being in different eutrophication levels are calculated to achieve water eutrophication risk assessment. The simulation results show that the proposed method can effectively estimate the unknown parameters of the water quality mechanism model, predict water quality evolution and assess the eutrophication risk with better accuracy and rationality by comparing the existing methods.