Coupled Data-Driven Evolutionary Algorithm for Toxic Cyanobacteria (Blue-Green Algae) Forecasting in Lake Kinneret
Publication: Journal of Water Resources Planning and Management
Volume 141, Issue 4
Abstract
Cyanobacteria blooming in surface waters have become a major concern worldwide, as they are unsightly, and cause a variety of toxins, undesirable tastes, and odors. Approaches of mathematical process-based (deterministic), statistically based, rule-based (heuristic), and artificial neural networks have been the subject of extensive research for cyanobacteria forecasting. This study suggests a new framework of linking an evolutionary computational method (a genetic algorithm) with a data driven modeling engine (model trees) for external loading, physical, chemical, and biological parameters selection, all coupled with their associated time lags as decision variables for cyanobacteria prediction in surface waters. The methodology is demonstrated through trial runs and sensitivity analyses on Lake Kinneret (the Sea of Galilee), Israel. Model trials produced good matching as depicted through the results correlation coefficient on verification data sets. Temperature was reconfirmed as a predominant parameter for cyanobacteria prediction. Model optimal input variables and forecast horizons differed in various solutions. Those in turn raised the problem of best variables selection, pointing towards the need of a multiobjective optimization model in future extensions of the proposed methodology.
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Acknowledgments
This research was supported by the Fund for the Promotion of Research at the Technion, and by the Technion Grand Water Research Institute (GWRI). Data for this study was obtained through the courtesy of Mekorot-Israel National Water Company Co. and the Kinneret Limnological Laboratory (KLL), Israel Oceanographic & Limnological Research Ltd. (IOLR). The authors extend special gratitude to Dr. Alon Rimmer and Yury Lechinsky from KLL for providing meteorological data for this research.
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Journal of Water Resources Planning and Management
Volume 141 • Issue 4 • April 2015
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© 2014 American Society of Civil Engineers.
History
Received: Jan 16, 2014
Accepted: Mar 4, 2014
Published online: Jul 24, 2014
Discussion open until: Dec 24, 2014
Published in print: Apr 1, 2015
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