%0 Journal Article %1 chan:2007:WR %A Chan, Wai Sum %A Recknagel, Friedrich %A Cao, Hongqing %A Park, Ho-Dong %D 2007 %J Water Research %K Clustering, Explanation Forecasting, Lake Microcystin, Microcystis, Ordination, Suwa, algorithms, genetic programming, %N 10 %P 2247--2255 %R doi:10.1016/j.watres.2007.02.001 %T Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms %V 41 %X Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r2=0.74 for testing.

@article{chan:2007:WR, abstract = {Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r2=0.74 for testing.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Chan, Wai Sum and Recknagel, Friedrich and Cao, Hongqing and Park, Ho-Dong}, biburl = {https://www.bibsonomy.org/bibtex/2235e697e77afa3f55cbbf2f8cab63cbe/brazovayeye}, doi = {doi:10.1016/j.watres.2007.02.001}, interhash = {99a9f2d9b6bc98bf147856fd5b6bb2d1}, intrahash = {235e697e77afa3f55cbbf2f8cab63cbe}, journal = {Water Research}, keywords = {Clustering, Explanation Forecasting, Lake Microcystin, Microcystis, Ordination, Suwa, algorithms, genetic programming,}, month = May, notes = {a School of Earth and Environmental Sciences, University of Adelaide, Adelaide 5005, Australia b Cooperative Research Centre for Water Quality and Treatment, Salisbury 5108, Australia c Department of Environmental Sciences, Shinshu University, Matsumoto 390-8621, Japan}, number = 10, pages = {2247--2255}, timestamp = {2008-06-19T17:37:30.000+0200}, title = {Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms}, volume = 41, year = 2007 }