INPUT SELECTION BY EPR-MOGA

The growing availability of field data, from information and communication technologies (ICTs) in "smart'' urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multi-objective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure

Traitement et analyse de séries chronologiques continues de turbidité pour la formulation et le test de modèles des rejets urbains par temps de pluie

Des approches parcimonieuses sont aujourd'hui développées pour la modélisation de la qualité des rejets urbains par temps de pluie, e adéquation avec la quantité de données disponibles. De plus, l'analyse des incertitudes apparaît comme un outil incontournable pour le test des modèles. Parallèlement, le développement des techniques de mesure en continu en réseau, spectrométrie et turbidité, permet l'obtention de données continues de flux de matières en suspension et de demande chimique en oxygène en grand nombre, apportant une information riche. Ce travail constitue une des premières études en hydrologie urbaine basée sur l'exploitation d'une grande base de données acquises par la mesure de la turbidité. Des mesures sur la période 2004-2008 ont été exploitées sur deux sites. Après traitement et validation, 263 et 239 événements pluvieux ont été retenus. L'analyse des données a permis la formulation d'hypothèses sur la génération des flux pour la proposition de modèles adaptés. Le test de l'approche multi-régression a confirmé la nécessité de construire des modèles locaux, basés sur une analyse approfondie des données. Les meilleurs modèles obtenus sont ceux pour la masse événementielle qui parviennent à reproduire en tendance la variabilité des observations. La méthode bayésienne a été utilisée pour le test d'un modèle d'Accumulation-Erosion-Transfert simple à l'échelle du bassin versant. Les premiers résultats mettent e défaut la structure du modèle testé. Cependant ces premiers tests ont démontré l'efficacité de la procédure d'analyse bayésienne, dont l'application du principe d'apprentissage permet d'améliorer de manière significative les structures des modèles.More and more urban water managers are tackling the issue of water quality modelling. Current research works focus on parsimonious modelling approaches that match the amount of data available for calibration. Moreover uncertainties analysis now appears as an integrated step and a powerful tool in models testing. In parallel, development of in sewer continuous measurements based on spectrometry and turbidimetry techniques, provides large data base of continuous total suspended solids and chemical oxygen demand concentrations, providing much information on fluxes dynamics. This research work is one of the first studies in urban hydrology based on a large turbidity data base. Data from two sites have been treated and validated, with measurements over the period 2004-2008. 263 and 239 stormwater events were selected for the modelling work for the two sites. Data analysis provided insights for making assumptions on the pollutant fluxes generation and proposing adapted models. Test of multi-regression approach that it is necessary to build local approaches based on detailed data analysis. Best models were obtained for event mass, data variability could be reproduced in trend. Formal Bayesian approach was used for testing a simple global Accumulation-Erosion-Transfer model for Chassieu. First results evidenced the difficulties of the model to reproduce the dynamics variability. This may be due to the simple structure. However these first tests have demonstrated the efficiency of the Bayesian analysis procedure. ln particular, the application of the learning principle showed that model structure can be significantly and efficiently improved.VILLEURBANNE-DOC'INSA LYON (692662301) / SudocVILLEURBANNE-DOC'INSA-Bib. elec. (692669901) / SudocSudocFranceF

Model representation of the Sudanian hydrological processes : application on the Donga catchment (Benin)

During the AMMA (African Monsoon Multidisciplinary Analysis) program, intensive field experiments were conducted on the Donga catchment (586 km(2)), which is part of the Oueme surveyed hydrological watershed (14,400 km 2). Based on these studies, a number of general hydrological assumptions were derived to explain the hydrological functioning of catchments located in the Sudanian hydrological area of West Africa. To take advantage of this field-acquired knowledge in the study of the impacts of climate and anthropogenic changes in these catchments, a model (TOPAMMA) was derived based on these hydrological assumptions. Subsurface lateral fluxes were described in the model using the TOPMODEL framework. The recharge of the deep water table was also modelled, taking into account its disconnection from the river network. Simple geomorphotogic approaches were used to estimate the time-transfer of both surface and subsurface water fluxes. Finally, to be consistent with the available meteorological data, a simple parameterization of evapotranspiration was added to the model. This paper details this modelisation as well as its corroboration on the Donga catchment. The data collected over the catchment during the 2002-2004 periods was therefore used at different scales, within either a quantitative or qualitative perspective. The results show that the model representation of the water cycle is quite realistic, which allows the AMMA community to have a useful tool available for water balance studies on the Sudanian region. However, further field investigations are necessary to confirm main model assumptions. Finally, the process representation in the model is now improved, especially with regard to the description of spatial land-surface heterogeneities and surf ace-atmosphere interactions