Technical faculty in Bor, University of Belgrade, Vojske Jugoslavije 12, 19210, Bor, Serbia.
Environ Monit Assess. 2018 Jun 27;190(7):434. doi: 10.1007/s10661-018-6814-0.
In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.
为了优化采样、监测和管理过程，本文的最初目的是开发一种用于定义和预测水质时间变化的模型。在摩拉瓦河流域（塞尔维亚）的情况下，通过应用不同的多元统计技术来识别时间变化模式。聚类分析的结果表明存在三个监测期：枯水期、过渡期和丰水期，这与分析河流流域的水流变化相符。通过进行主成分/因子分析，研究了减少初始数据集和识别主要污染源的可能性。结果表明，自然因素在时间组中具有主导影响。为了识别有区别的水质参数，进行了判别分析（DA）。通过提取两个参数（水温、电导率）进行 DA 分析，实现了数据集的显著减少。此外，还使用人工神经网络技术来测试在监测期内预测判别因子值变化的可能性。该方法已被证明可用于预测所有时间聚类中提取参数的时间变化的可靠性。
