Water networks are spatially dispersed, easily accessible, and vulnerable to contaminating intrusions. If contamination is detected too late, then damage to the population may be irreversible. For the hard task of optimal sensor placement, this work presents a multi-objective approach that is combined with post-processing methods for a Pareto front analysis. The contamination is represented by the chemical reactions of the pesticide Parathion in water quality simulations. A multi-objective approach is used that incorporates four contamination probability functions. The Pareto front is analyzed with a clustering approach, and a coverage matrix is used to evaluate the centers of each cluster. An automatic selection solution method, based on distances to the most suitable solution, is also explored.

水网络在空间上分散，易于访问，并且容易受到污染入侵的影响。如果发现污染太迟，那么对种群的损害可能是不可逆的。对于最佳传感器放置的艰巨任务，这项工作提出了一种多目标方法，该方法与后处理方法相结合以进行帕累托锋线分析。在水质模拟中，农药对硫磷的化学反应代表了污染。使用了包含四个污染概率函数的多目标方法。使用聚类方法分析帕累托锋，并使用覆盖矩阵评估每个聚类的中心。还探讨了基于到最合适解的距离的自动选择解方法。