近年来,智能水传感技术在水管理中发挥了至关重要的作用,满足水资源分析有效监测和控制的迫切需要。智能水传感技术的挑战在于确保传感器收集的数据的可靠性和准确性。异常值是智能传感中众所周知的问题,因为它们会对有用分析的可行性产生负面影响,并使评估相关数据变得困难。在这项研究中,我们评估了四种传感器的性能:电导率(EC),溶解氧(DO),温度(Temp),和pH。我们实现了四种经典的机器学习模型:支持向量机(SVM),人工神经网络(ANN),决策树(DT),和基于孤立森林(iForest)的离群值检测,作为可视化数据之前的预处理步骤。该数据集是由安装在布鲁塞尔湖泊的实时智能水传感监测系统收集的,河流,池塘。得到的结果清楚地表明,支持向量机优于其他模型,显示98.38%的pH值F1得分率,温度为96.98%的F1得分率,DO的F1得分为97.88%,EC的F1得分为98.11%。此外,人工神经网络也取得了显著的成果,将其确立为可行的替代方案。
In recent years, smart water sensing technology has played a crucial role in water management, addressing the pressing need for efficient monitoring and control of water resources analysis. The challenge in smart water sensing technology resides in ensuring the reliability and accuracy of the data collected by sensors. Outliers are a well-known problem in smart sensing as they can negatively affect the viability of useful analysis and make it difficult to evaluate pertinent data. In this study, we evaluate the performance of four sensors: electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH. We implement four classical machine learning models: support vector machine (SVM), artifical neural network (ANN), decision tree (DT), and isolated forest (iForest)-based outlier detection as a pre-processing step before visualizing the data. The dataset was collected by a real-time smart water sensing monitoring system installed in Brussels\' lakes, rivers, and ponds. The obtained results clearly show that the SVM outperforms the other models, showing 98.38% F1-score rates for pH, 96.98% F1-score rates for temp, 97.88% F1-score rates for DO, and 98.11% F1-score rates for EC. Furthermore, ANN also achieves a significant results, establishing it as a viable alternative.