Jaroslav Cerni Institute for Development of Water Resources, Jaroslava Cernog 80, 11226 Belgrade, Serbia.
Innovation Center of the Faculty of Technology and Metallurgy, Karnegijeva 4, 11120 Belgrade, Serbia.
Sci Total Environ. 2019 Mar 1;654:1000-1009. doi: 10.1016/j.scitotenv.2018.11.189. Epub 2018 Nov 14.
Rationalization of water quality monitoring stations nowadays is applied in many countries. In some cases, missing data from abandoned/inactive stations, spatial and temporal, could be very important, hence the use of artificial neural networks (ANNs) for virtual water quality monitoring at inactive monitoring sites was investigated. The aim was to develop single-output and simultaneous ANNs for the spatial interpolation of 18 water quality parameters at single- and multi-inactive monitoring sites on Danube River course through Serbia. Those different modeling approaches were considered in order to determine the most suitable combination of models. The variable selection and sensitivity analysis in the case of simultaneous models were performed using a modified procedure based on Monte Carlo Simulations (MCS). In general, the multi-target models tend to be more accurate than single target ones, while single output models outperform the simultaneous ones. Hence, for particular monitoring network and set of water quality parameters the optimal combination of models must be defined based on model's accuracy and computational effort needed. The MCS selection procedure has proved to be efficient only in the case of simultaneous multi-target model. MCS based analysis of input-output interactions has shown all significant interactions in the case of simultaneous single-target are grouped as a complex cluster of interactions, where majority of inputs influence on several outputs. In the case multi-target model those interactions were portioned in five separate clusters, there majority of them mimic the input-output interactions that are present in single output models. The modeling strategy for study area was proposed on the basis of the performance of created models (mean average percentage error < 10%): simultaneous multi-target model for pH, alkalinity, conductivity, hardness, dissolved oxygen, HCO, SO and Ca, single-output multi-target models for temperature and Cl, simultaneous single-target models for Mg and CO, single output single target models for NO.
如今，水质监测站的合理化在许多国家都得到了应用。在某些情况下，废弃/不活跃站点的数据缺失（时空）可能非常重要，因此研究了使用人工神经网络 (ANNs) 对不活跃监测站点的虚拟水质监测。目的是通过塞尔维亚多瑙河河道上的单个和多个不活跃监测站点，开发用于 18 个水质参数的单输出和同时输出的 ANN，进行空间插值。为了确定最适合的模型组合，考虑了不同的建模方法。在同时模型的情况下，使用基于蒙特卡罗模拟 (MCS) 的修改过程进行变量选择和敏感性分析。一般来说，多目标模型往往比单目标模型更准确，而单输出模型优于同时模型。因此，对于特定的监测网络和水质参数集，必须根据模型的准确性和所需的计算工作量来定义最佳模型组合。MCS 选择过程仅在同时多目标模型的情况下证明是有效的。基于 MCS 的输入-输出相互作用分析表明，同时单目标模型中所有显著的相互作用都被分组为一个复杂的相互作用簇，其中大多数输入会影响多个输出。在多目标模型的情况下，这些相互作用被分为五个单独的簇，其中大多数相互作用模拟了单输出模型中存在的输入-输出相互作用。基于创建模型的性能（平均平均百分比误差<10%）提出了研究区域的建模策略：同时多目标模型用于 pH 值、碱度、电导率、硬度、溶解氧、HCO、SO 和 Ca，单输出多目标模型用于温度和 Cl，同时单目标模型用于 Mg 和 CO，单输出单目标模型用于 NO。
