Optimal experimental design for decision-making with dynamic water quality models

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Dynamic models increasingly become key ingredients of decision-making in water infrastructure planning and receiving water management. It is generally accepted that within the three preparatory steps of a modelling exercise, (i) definition of the objective, (ii) structuring of prior knowledge and (iii) analysis of available data, most modelling issues are raised due to limitations in quantity and quality of the available data. It is therefore surprising to have to conclude from the current state-of-practice that relatively little research effort is conducted to provide methodological support to collect good quality data within the constraints imposed by available resources (time, funding, people, equipment, etc.). This discovery grant proposal therefore aims at developing such methodological support in the form of what is termed "model-based optimal experimental design" (mbOED). This will be developed for two types of dynamic systems where water quality aspects are central: biological wastewater treatment and river water quality. Decision-making always occurs under uncertainty and the sources of uncertainty can be attributed to a number of sources, e.g. the model structure, the parameter values used and the input data. mbOED can assist in a methodological reduction of these uncertainties. The discovery grant research proposal thus has three research lines: (i) OED for Model Discrimination (mbOED/MD), (ii) OED for Parameter Estimation (mbOED/PE) and (iii) OED for overall reduction of uncertainty in Decision Variables (mbOED/DV). Thus, the proposed research programme should deliver a methodological framework by which experiments can be designed that allow (i) deciding on the best model structure, (ii) obtaining low uncertainty parameter estimates and (iii) predicting decision variables with least possible uncertainty. The work will be supported experimentally by CFI grants for river measuring stations and a wastewater treatment pilot plant.