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La nouvelle génération de logiciels destinés aux études d'assainissement dispose d'un nombre croissant de modèles hydrauliques et hydrologiques. Il en découle une augmentation des possibilités de choix parmi ces modèles qui complique la tâche des techniciens de l'assainissement. Pour limiter cette difficulté, nous suggérons d'introduire dans les logiciels des outils permettant d'aider les utilisateurs à choisir les modèles en adéquation avec le réseau à simuler. Dans cet article, nous nous intéresserons essentiellement aux modèles de propagation en conduite. Les modèles de propagation les plus usités sont un modèle basé sur les équations de Barré de Saint Venant et des modèles conceptuels nettement plus simples tels que le modèle Muskingum. Ces modèles présentent chacun des avantages et des inconvénients. Dans la pratique, plus un modèle est sophistiqué, mieux il est capable de représenter la réalité. En contrepartie, il est plus difficile à utiliser et nécessite davantage de données et des temps de calcul plus importants. Le problème qui se pose alors à l'utilisateur est de décider quel modèle utiliser. Pour régler ce problème, nous proposons un système d'aide au choix prenant en compte les caractéristiques du réseau étudié, les événements pluvieux simulés et le type d'étude réalisée. Les connaissances nécessaires pour cette aide au choix de modèles peuvent être de qualité variable. Pour mesurer la confiance à accorder à ces connaissances, il est nécessaire de prendre en compte les notions d'imprécision et d'incertitude. Cet état de fait nous a conduit, lors de l'élaboration de cet outil d'aide au choix des modèles de propagation en conduite, à définir un ensemble de règles utilisant la théorie des sous-ensembles flous., Most hydraulic and hydrologic softwares offer an increasing choice of models, each with its advantages and disadvantages. Generally, the more sophisticated the model, the better it can represent larger aspects of reality, but also the more difficult it is to use and the longer the data acquisition and calculation times are. In fact, the real difficulty lies in selecting the appropriate model to use. To answer this question, two subproblems must be solved : - what is the validity field for each of the models, with respect to the network structure, operating conditions, type of rainfall, nature of problem, etc. ? - once this information is available, what is the best way to ensure its usability, even by people who are not experts in hydrology or hydraulics ? The first problem can be dealt with by analyzing the theoretical validity field of the different models. Nevertheless, this method raises certain difficulties. It requires a particularly thorough knowledge of the equations, algorithms and calculating artifices used in the software package. But, for various reasons, software designers generally refuse to supply this information. Until now, the solution to the second problem has only been addressed by means of scientific reports, communications or papers. The published data generally gives a global introduction to the validity field of each model. Experience shows that most software users do not have enough information on this subject, or, even if they do, do not use it correctly. To work towards solving these two problems, we propose to introduce into the software packages a decision support system which can help to choose the best model according to the simulated network. In this paper, only the models for flow simulation will be taken into account. Presently, the most commonly used models are the more or less complete Barre de Saint Venant equations and more simple conceptual models like Muskingum model. The major difficulty in solving the first subproblem, was mainly the collection and reformulation of pre-existing knowledge. Considerable bibliographical work had to be supplemented by interviews of experts and by complementary studies (Semsar, 1995) (Mottie, 1996). The result of these studies was the identification of a set of criteria related to the network (slope, fractal dimension, loop index, etc.) or to the working conditions depending on the rainfall event (fullness rate, travel rate, etc.). The answer to the second problem was to develop an "intelligent" man-machine interface able to analyse the background of the simulation (values of the criteria) and to advise the user on the model to select. The knowledge required to build this decision support system can vary in both source and quality, so assessment of its reliability involve the notions of uncertainty and inaccuracy. The problem of uncertainty has been solved by associating uncertainty degrees to the rules. These degrees define a proposal's level of reliability. The approach to inaccuracy is based on the theory of fuzzy sets, according to which the membership of an element of a given set is not settled but relative. The validity of a given fact is represented by a value between 0 (false) and 1 (true). This value can be related to a variable by fuzzy rules, represented by trapezoidal intervals. By this way, each of the criteria has been represented by qualitative decision variables which allow the elaboration of qualitative rules, leading to a "probably better" decision. One more decision variable - the kind of study - has been added because the hydrograph at the outlet of the network is not necessarily the only criterion to be taken into account. Each of the decision variable is represented by one, two, or sometimes more possible qualitative characterisation(s), associated with a degree of possibility (not probability because the sum of all the possibilities is not necessarily equal to 1). The decision support system uses these variables in a set of rules to determine the degree of adequacy of each model. The development of this system shows us that the use of fuzzy sets and qualitative rules seems to be well adapted to represent the used knowledge. The decision support system will be installed in a software package called CANOE developed by INSA de Lyon and SOGREAH. In the future we envisage adding an explanatory unit to the decision support system. This study showed too that there is some lack in the knowledge about flow simulation models, so it seems useful to continue to study the validity field of each model.

Le succès d'une gestion des écosystèmes naturels requiert une connaissance approfondie des différents processus qui interviennent et de leurs échelles de temps et d'espace particulières. Pour cette raison, les décideurs ont besoin d'analyser une vaste gamme de données et d'informations géographiques. Les modèles mathématiques, les systèmes d'informations géographi-ques et les systèmes experts sont capables de produire cette analyse, mais seule une minorité de gestionnaires les utilise actuellement. Cet article identifie quelques unes des raisons à l'origine de l'hésitation des gestionnaires à adopter de tels outils d'aide à la décision pour la gestion des ressources naturelles et propose une structure qui pourrait faciliter leur utilisation pour le processus de prise de décision. Cet exercice est réalisé à l'intérieur du contexte de la gestion intégrée par bassin. Une revue des systèmes d'aide à la décision est également présentée., Many methods of integrated or watershed management exist which account for the necessary biophysical and socio-economic factors at the watershed level. Some of these approaches are ecosystem oriented while others are socio-economically oriented. Whatever the definition, water management at the watershed level needs to account for a plenitude of variables related to the air, water, soil, biology, and economy. The successful management of natural ecosystems requires a thorough understanding of their characteristic time and spatial scales. Because of this, decision makers need to analyze a wide range of data and geographic information. Mathematical models, geographic information systems and expert systems are capable of performing this analysis, but only a minority of managers are currently using them. This paper identifies some of the reasons why ecosystem managers have been slow to adopt such decision support tools in natural resources management and proposes a framework to facilitate their use in the decision making process. This is done in an integrated watershed management context. A review of related decision support systems is also presented.Four types of decision-support tools are introduced : mathematical models, expert-systems, geographical information systems (GIS) and decision support systems (DSS). Mathematical models have long been used for simulation, prediction, and forecasting, however, they are often task specific and were rarely developed for management uses. GIS are more and more commonly being used for decision support as they become more affordable and user-friendly and are very well-suited for managing resources at a spatial scale. There exist many kinds of software ranging from a simple viewer used for cartographic purposes to complex GIS oriented toward spatial analysis and modelling. Expert systems are also interesting for decision support when specific goals are being considered. Finally, DSS are perhaps the digital tools most applicable to management purposes, often integrating one or more models, a GIS or expert system functionalities. There are two types of DSS : 1. Environmental Information Systems (EIS), and 2. Integrated Modelling Systems (IMS) EIS can be very user- friendly, relying heavily upon GIS and statistical functions.IMS also use GIS capabilities, but integrates several mathematical models as well. The level of integration between models varies considerably and the complexity of IMS are generally high.Two questions underlie the operational use of digital technologies for decision support. The first is whether or not such technology should be used at all, while the second is why such tools take time to be adopted by government and management agencies. The use of digital technologies is often required when the problem is complex and where there are a wide range of factors involved with different spatial and temporal scales. Three major constraints towards the implementation of decision support tools can be pinpointed :1. technology, 2. data, and 3. working organization. Technological constraints include cost, lack of user friendliness, and hardware problems, among other factors. Data constraints are mostly related to availability, cost, heterogeneity and volume. Finally, organization constraints pertain mostly to the manager's perception of the tool and the structural integration of the tool within the decision process.This paper proposes a 4-step approach to optimize the use of decision-support tools. The first step requires that managers and decision-makers clearly define their project, goals and budget, as well as, decide whether to use an integrated watershed management approach or a more discrete approach. This leads directly to the second step, which consists of choosing the most appropriate digital support tool. This requires communication between managers and scientists, and at this point, data gathering and integration should begin. The third phase consists of the development of a new tool or adaptation of an existing one within the context of the agency's management structure. The final step is the operational use of the decision support tool by the agency, following an initial trial period. The successful use of a decision support tool for management purposes depends on proper planning that accounts for all factors related to management needs, budget, data, ease of use, and organization integration.