Journal of Freshwater Ecology · 2004 · 32 citations · 13 references
Life SciencesEcologyEnvironmental SciencesBiological Systems ModelingEnvironmental ModelingFreshwater EcologyAlgal EcologyAlgal PhysiologyEcological ModelingEcological System ModelingEnvironmental Systems ModelingLake EcologyAlgal Bloom ModelingAlgal BloomsBloom ForecastingBloom Prediction ModelsChemical Oxygen DemandChemical Oxygen Demand AnalysisEutrophication ModelingFreshwater Cyanobacteria
Compared with other approaches for modeling and predicting, artificial neural networks are more effective in describing complex and non-linear systems.The occurrence of cyanobacterial blooms has been a continuous and serious problem over the past decades in hypereutrophic Lake Dianchi.Yet, the main factor(s) initiating these blooms remain(s) unclear.During 2001-2002 at 40 sampling sites in Lake Dianchi, physicochemical parameters possibly relating to the blooms were measured.Parameters directly or indirectly relating to the cyanobacterial blooms were used as driving factors in a back-propagation network to model the concentration of chlorophyll a.According to sensitivity analysis, chemical oxygen demand was identified as a very significant environmental factor for algal growth in Lake Dianchi.
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Neural Networks and the Bias/Variance Dilemma
Stuart Geman, Elie Bienenstock, René Doursat · Neural Computation · 1992 · 3.6K citations
Interpreting neural-network connection weights
G. David Garson · AI Expert archive · 1991 · 1.5K citations
Application of neural networks to modelling nonlinear relationships in ecology
Sovan Lek, M. DELACOSTE, Philippe Baran et al. · Ecological Modelling · 1996 · 714 citations