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<identifier>oai:HAL:hal-00847582v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Evolutionary predictive modelling for flash floods</title>
<creator>Segretier, Wilfried</creator>
<creator>Collard, Martine</creator>
<creator>Clergue, Manuel</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>IDC ; Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG) - Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>Congress on Evolutionary Computation (CEC) 2013</source>
<coverage>Cancun, Mexico</coverage>
<identifier>hal-00847582</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00847582</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00847582</source>
<source>Congress on Evolutionary Computation (CEC) 2013, Jun 2013, Cancun, Mexico. pp.844 - 851, 2013, 〈10.1109/CEC.2013.6557656〉</source>
<identifier>DOI : 10.1109/CEC.2013.6557656</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1109/CEC.2013.6557656</relation>
<language>en</language>
<subject lang=en>Evolutionary computation</subject>
<subject lang=en>Flood prediction</subject>
<subject lang=en>Data mining</subject>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>Modelling techniques for river hydrologic forecasting systems have taken advantage of machine learning methods especially for flood prediction. But current solutions, mostly based on artificial neural networks do not always meet end users requirements on the readability and the understandability of predictive models. In this paper, we present a new version of our original solution based on the concept of aggregate variables in order to predict flash flood events from observed water level and/or rain measurements, particularly in the context of caribbean watersheds in which flash flood are much uncertain. We combine aggregate variables in juries. Juries of aggregate variables are trained and tested using a typical 10-fold cross validation scheme. Best juries are searched through an evolutionary approach that is optimized. Different parameters are set up like aggregation periods and jury sizes to prove the efficiency of the proposed approach compared to classical solutions.</description>
<date>2013-06-20</date>
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