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.

中文翻译：

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基于化学计量分析和突变理论模型的蓝藻水华暴发预警

蓝藻水华主要由氮、磷等化学因素引起，可在水中产生有毒物质，甚至减少生物多样性。迫切需要遏制蓝藻水华并对其爆发进行早期预警。本文提出了一种基于化学计量分析的蓝藻生长非线性数学模型。通过布谷鸟搜索智能算法对蓝藻生长非线性数学模型中的参数进行估计和优化，提高估计精度。基于尖点突变理论的非线性数学模型的分岔集预测蓝藻水华爆发的时间。某些天然湖泊监测数据采用所提出的方法进行处理以进行说明。结果表明，非线性模型的分岔集可以准确地预测蓝藻水华爆发的时间。因此，所提出的方法可以有效地早期预警蓝藻水华爆发。