Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation

Real-time observation, early warning and forecasting phytoplankton blooms by integrating in situ automated online sondes and hybrid evolutionary algorithms

“…Because HEA can provide rules and equations to explain what factors drive the dynamics of the dependent variables (e.g. phytoplankton abundance), this algorithm has attracted much attention in forecasting the dynamics of aquatic ecosystems (Cao et al, 2006;Recknagel, 2013;Recknagel et al, 2013Recknagel et al, , 2014Wai et al, 2007).…”

“…HEA combines genetic programming (GP) for optimizing the model structure and different genetic algorithms (GA) for optimizing the model parameters Recknagel et al, 2014). GP can select best input variables and construct IF-THEN-ELSE rules of the models.…”

“…GP can select best input variables and construct IF-THEN-ELSE rules of the models. There are three function sets in GP constructed rules, including logic functions (FL = {AND, OR}), comparison functions (FC = {N, b, ≥, ≤}), and arithmetic functions (FA = {+, −, * , /, exp, ln}) (Recknagel et al, 2014). In forecasting phytoplankton dynamics, Hill Climbing (HC) and Differential Evolution (DE) algorithms are reported as the most popular GAs for optimizing the parameters of IF-THEN-ELSE formula .…”

“…In the past decades, a wide variety of predictive models and algorithms, including physical-process based models (Jorgensen and Nielsen, 1994;Wu and Xu, 2011), artificial neural network (Jeong et al, 2006;Recknagel et al, 2006;, and evolutionary algorithms (Recknagel, 2013;Recknagel et al, 2013Recknagel et al, , 2014, have been developed to forecast the dynamics of phytoplankton. Since algal blooms are considered as a result from an excess of nutrients, particularly nitrogen (N) and phosphorus (P) (Conley et al, 2009), most of the existing models emphasize nutrient availability (e.g.…”

Molecular separation of two long taxonomically debated cyanobacterial genera Cylindrospermopsis and Raphidiopsis (Nostocales) based on the ITS-L phylogeny

“…Blooms of the cyanobacterial genus Cylindrospermopsis Seenayya et Subba Raju have recently been exacerbated because if the eutrophication caused by anthropogenic activities and global climate change (Chapman and Schelske, 1997;Padisá k, 1997;Briand et al, 2004;Gemelgo et al, 2008;Wu et al, 2011;Dobberfuhl, 2003;Karadžić et al, 2013;Lei et al, 2014;Recknagel et al, 2014). This genus morphologically resembles another cyanobacterial bloom-forming genus, Raphidiopsis Fritsch et Rich (Li et al, 2001;Moustaka-Gouni et al, 2009;Alster et al, 2010;Wu et al, 2011;Komá rek, 2013).…”