Early warning of cyanobacteria blooms outbreak based on stoichiometric analysis and catastrophe theory model

Abstract

Cyanobacteria bloom, mainly caused by chemical factors such as nitrogen and phosphorus, can produce toxic substances in water or even reduce biodiversity. It is urgent to curb the cyanobacteria bloom and early warn their outbreak. This paper proposes a nonlinear mathematical model of cyanobacteria growth based on stoichiometric analysis. Parameters in the cyanobacteria growth nonlinear mathematical model are estimated and optimized by the cuckoo search intelligent algorithm to improve the estimation accuracy. Time of cyanobacteria blooms outbreak is forecasted by bifurcation sets of the nonlinear mathematical model based on cusp catastrophe theory. Certain natural lake monitoring data is processed with the proposed method for illustration. The results show that time of cyanobacteria blooms outbreak is forecasted accurately by the bifurcation sets of the nonlinear model. Hence, cyanobacteria blooms outbreak can be early warned effectively by the proposed method.