Abstract
The number of observations is limited by budgetary and other constraints, but the collected data must be sufficient to meet a wide range of monitoring objectives. The process of determining an efficient temporal water quality monitoring design has been formulated as an optimization problem. In the problem, a target level of acceptable uncertainty for a given indicator is specified, and the resultant solution is the smallest set of observation dates sufficient to achieve this level. Solution robustness can be improved by adding a sliding window – a maximum range in deviations of the observation dates that still provide the acceptable level of uncertainty. Application of the proposed approach to a real-world case study suggested an iterative procedure for the improvement of monitoring designs.
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Erechtchoukova, M.G., Chen, S.Y., Khaiter, P.A. (2009). Application of Optimization Algorithms for the Improvement of Water Quality Monitoring Systems. In: Athanasiadis, I.N., Rizzoli, A.E., Mitkas, P.A., Gómez, J.M. (eds) Information Technologies in Environmental Engineering. Environmental Science and Engineering(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88351-7_13
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DOI: https://doi.org/10.1007/978-3-540-88351-7_13
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