Water pollution causes an ever-increasing number of diseases and represents a worldwide concern, both for governments and researchers, as well as public opinion. This pollution also regards drinkable water, with two billion people plagued by this problem. Therefore, it is crucial to find reliable and low-cost technologies for a continuous and diffused monitoring of water. In this paper, we present a novel approach that allows the detection of water contaminants by using an ad-hoc classification system that can be implemented aboard low-cost sensors. To this aim, we first project the input data from the sensors into a 3-D space by using the PCA algorithm, then we use an ad-hoc devised classifier to distinguish the contaminants in the transformed space. We used an evolutionary algorithm to learn the parameters of the classifiers. The experiments were performed on a large dataset containing data from four contaminants, with the phosphoric and sulphuric acids, among the others. The results obtained confirm the effectiveness of the proposed approach.

水污染导致越来越多的疾病，并成为世界各国政府和研究人员以及公众舆论关注的问题。这种污染也涉及饮用水，有20亿人受此问题困扰。因此，找到可靠且低成本的技术来连续和分散地监测水至关重要。在本文中，我们提出了一种新颖的方法，该方法允许使用可在低成本传感器上实现的临时分类系统来检测水污染物。为此，我们首先使用PCA算法将来自传感器的输入数据投影到3-D空间中，然后使用临时设计的分类器来区分转换后的空间中的污染物。我们使用了一种进化算法来学习分类器的参数。实验是在一个大型数据集上进行的，该数据集包含来自四种污染物的数据，以及磷酸和硫酸等。获得的结果证实了该方法的有效性。