Méthodes statistiques pour l'évaluation et la reconfiguration des réseaux de suivi de la qualité de l'eau de surface.

Khalil, Bahaa (2010). Méthodes statistiques pour l'évaluation et la reconfiguration des réseaux de suivi de la qualité de l'eau de surface. Thèse. Québec, Université du Québec, Institut national de la recherche scientifique, Doctorat en sciences de l'eau, 333 p.

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Résumé

This study addresses the assessment and redesign of surface water quality monitoring (WQM) networks. The design of WQM networks depends primarily upon the objectives of the monitoring program and the characteristics of the monitored region. Despite several statistical approaches that have been proposed for the assessment and redesign of long-term WQM networks, several deficiencies in these approaches exist. The main goal of this study is to propose statistical approaches for the assessment and redesign of WQM networks that overcome existent deficiencies in the currently applied approaches. In addition, this study intends to introduce an innovative approach for the estimation of the water quality characteristics at ungauged sites. Four objectives are specified: (i) to review the current applied statistical approaches for the assessment and redesign of surface WQM networks; (ii) to develop a new statistical approach for the rationalization of water quality variables; (iii) to develop a new statistical approach for the assessment and redesign of WQM locations; and (iv) to introduce a statistical methodology for the estimation of water quality characteristics at ungauged sites. In this study, statistical approaches used for the assessment and redesign of surface water quality monitoring networks are first reviewed. In this review, various monitoring objectives and related procedures used for the assessment and redesign of surface WQM networks are discussed. For each approach, advantages and disadvantages are examined from a network design perspective. The literature review reveals that correlation-regression is the most common approach used to assess and eventually reduce the number of water quality variables in WQM networks. However, several deficiencies in this approach are identified. Based upon these identified deficiencies, a new statistical approach is proposed for the rationalization of water quality variables. The proposed approach overcomes deficiencies in the conventional correlation-regression approach and represents a useful decision support tool for the optimized selection of water quality variables. It allows for the identification of optimal combinations of water quality variables to be continuously measured and those to be discontinued. To reconstitute information about discontinued water quality variables, four record extension techniques are examined. Ordinary least squares regression (OLS), the line of organic correlation (LOC), the Kendall-Theil robust line (KTRL) and KTRL2, which is a modified version of the KTRL proposed in this study. The advantage of the KTRL2 is that it includes the advantage of LOC in maintaining variability in the extended records and the advantage of KTRL in being robust in the presence of extreme values. Monte-Carlo and empirical studies are conducted to examine these four techniques for bias, standard error of moment estimates and a full range of percentiles. The Monte-Carlo study showed serious deficiencies in the OLS and KTRL techniques, while the LOC and KTRL2 techniques have results that are nearly similar. Using real water quality records, the KTRL2 is shown to lead to better results than the other techniques. The literature review also reveals that several deficiencies in the approaches proposed for the assessment of monitoring locations exist. The deficiencies vary from one approach to another, but generally include: (i) ignoring the characteristics of the basin being monitored in the design approach; (ii) handling multivariate water quality data sequentially rather than simultaneously; (iii) focusing mainly on locations to be discontinued; and (iv) ignoring reconstitution of information at discontinued locations. A methodology that overcomes these deficiencies is proposed. In the proposed methodology, hybrid-cluster analysis is employed to identify groups of sub-basins with similar characteristics. A stratified optimum sampling strategy is then employed to identify the optimum number of monitoring locations in each of the sub-basin groups. An aggregate information index is employed to identify the optimal combination of locations to be discontinued. Results indicate that the proposed methodology allows the identification of optimal combinations of locations to be discontinued, locations to be continuously measured and sub-basins where monitoring locations should be added. To fulfill the last objective, two models are developed for the estimation of water quality mean values at ungauged sites. An ensemble artificial neural network (EANN) model is developed to establish the functional relationship between water quality mean values and basin attributes. The second model is based on canonical correlation analysis (CCA) and EANN. CCA is used to form canonical attributes space using data from gauged sites. Then, an EANN is applied to identify the functional relationships between water quality mean values and the attributes in the CCA space. A jackknife validation procedure is used to evaluate the performance of the two models. The results show that the developed models are useful for estimating the water quality status at ungauged sites. However, the CCA-based EANN model performed better than the EANN model in terms of prediction accuracy.

Type de document:	Thèse Thèse
Directeur de mémoire/thèse:	Ouarda, Taha B. M. J.
Co-directeurs de mémoire/thèse:	St-Hilaire, André
Mots-clés libres:	statistique; réseaux de suivi; qualité; eau de surface; estimation; site non jaugé; rationalisation des variables; refonte
Centre:	Centre Eau Terre Environnement
Date de dépôt:	17 févr. 2014 21:44
Dernière modification:	04 mai 2023 18:15
URI:	https://espace.inrs.ca/id/eprint/1789

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