Wetlands are artificial basins that exploit the capabilities of some species of plants to purify water from pollutants. The design process is currently long and laborious: such vegetated areas are inserted within the basin by trial and error, since there is no automatic system able to maximize the efficiency in terms of filtering. Only at the end of several attempts, experts are able to determine which is the most convenient configuration and choose up a layout. This paper proposes the use of an evolutionary algorithm to automate both the placement and the sizing of vegetated areas within a basin. The process begins from a random population of solutions and, evaluating their efficiency with an state-of-the-art fluid-dynamics simulation framework, the evolutionary algorithm is able to automatically find optimized solution whose performance are comparable with those achieved by human experts.

An Evolutionary Approach to Wetlands Design / Gaudesi, Marco; Marion, A.; Musner, T.; Squillero, Giovanni; Tonda, ALBERTO PAOLO. - STAMPA. - 7833:(2013), pp. 177-187. ((Intervento presentato al convegno 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2013 tenutosi a Vienna (Austria) nel 3-5 April 2013 [10.1007/978-3-642-37189-9_16].

An Evolutionary Approach to Wetlands Design

GAUDESI, MARCO;SQUILLERO, Giovanni;
2013

Abstract

Wetlands are artificial basins that exploit the capabilities of some species of plants to purify water from pollutants. The design process is currently long and laborious: such vegetated areas are inserted within the basin by trial and error, since there is no automatic system able to maximize the efficiency in terms of filtering. Only at the end of several attempts, experts are able to determine which is the most convenient configuration and choose up a layout. This paper proposes the use of an evolutionary algorithm to automate both the placement and the sizing of vegetated areas within a basin. The process begins from a random population of solutions and, evaluating their efficiency with an state-of-the-art fluid-dynamics simulation framework, the evolutionary algorithm is able to automatically find optimized solution whose performance are comparable with those achieved by human experts.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2507855
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