## 1 Introduction  
Water quality monitoring programs (WQMP) are essential in order to provide decision-makers with the necessary information to implement best management practices, they are also essential to gain citizen and stakeholder support by disseminating the relevant information [1; 2; 3]. WQMPs can be defined as a long-term, spatially distributed, standardized surveillance, and quality assessment of all the activities surrounding water quality monitoring. WQMPs also need to be updated continuously in order to respond to new knowledge needs and adapt to new technologies, policies, constraints, and opportunities in human, technical, and financial resources [4]. When planning a WQMP, the following issues must be addressed: (1) setting realistic and representative monitoring objectives, (2) determining sampling sites, (3) choosing water quality parameters, (4) establishing sampling frequency and recurrence, (5) considering logistics, (6) assessing technical, financial and human resources, (7) identifying information diffusion channels, and (8) assessing how the information can be put to use (move on to action) [5; 6; 7; 8]. When optimizing a WQMP, it is necessary to evaluate whether the existing WQMP (1) has covered all planning criteria, (2) if monitoring objectives have been met or have emerged, (3) if technical, financial and human resources have evolved, and been used adequately (4) whether changes in laboratories, laboratory methodologies, probes and field observations have occurred, etc. It is also of the utmost importance to verify whether the information produced within an existing WQMP has been properly channeled, put to use and prompted management action to protect water resources [7; 8; 9; 10]. The planning, management, and optimization of a WQMP is a complex process involving many decisions that must be documented and supported through decision-support systems and adequate data management systems [6; 7; 8; 11; 12]. In addition, the planning and optimization of WQMPs should be based on precise monitoring and optimization objectives [8; 13]. The literature stresses the fact that many WQMPs are based on imprecise and assumed monitoring objectives that fail to respond to representative or realistic knowledge needs. In addition, WQMPs are rarely optimized in hindsight to monitoring objectives that influenced the design of these networks, nor are WQMPs optimized according to clear-cut optimization objectives [6; 14; 15]. Many handbooks and guidelines exist to plan WQMPs and many optimization methods have been developed. However, they are not part of a holistic solution addressing every aspect of WQMP planning, management and optimization [8]. Therefore, it was suggested that a holistic decision support tool should be developed to account for all steps in planning, managing, and optimizing WQMPs [6; 8]. The conceptual model of such an Intelligent Decision-Support System (IDSS) was developed and connected to a previously developed database Enki(tm) [12]. The purpose of this paper is to test some of the features of the IDSS implemented and test decision-support workflows prior to implementation. More precisely, the optimization process proposed by the IDSS is tested herein using the data of two watershed case studies from the province of Quebec, Canada. The optimization process is based on a selection of optimization objectives elicited through a participative approach conducted on both case studies [16]. The IDSS was designed to propose existing optimization methods from the literature which correspond to different types of optimization objectives, such as evaluating the number of sampling sites, water quality parameters and sampling frequency [13; 17; 18; 19].