Department of General Chemistry and Ecology, Kazan National Research Technical University Named after A.N. Tupolev-KAI, 10 K. Marx St., Kazan 420111, Russia.
Department of Applied Mathematics and Computer Science, Kazan National Research Technical University Named after A.N. Tupolev-KAI, 10 K. Marx St., Kazan 420111, Russia.
Sensors (Basel). 2023 Jul 5;23(13):6160. doi: 10.3390/s23136160.
The regulation of the anthropogenic load on waterbodies is carried out based on water quality standards that are determined using the threshold values of hydrochemical indicators. These applied standards should be defined both geographically and differentially, taking into account the regional specifics of the formation of surface water compositions. However, there is currently no unified approach to defining these regional standards. It is, therefore. appropriate to develop regional water quality standards utilizing modern technologies for the mathematical purpose of methods analysis using both experimental data sources and information system technologies. As suggested by the use of sets of chemical analysis and neural network cluster analysis, both methods of analysis and an expert assessment could identify surface water types as well as define the official regional threshold values of hydrochemical system indicators, to improve the adequacy of assessments and ensure the mathematical justification of developed standards. The process for testing the proposed approach was carried out, using the surface water resource objects in the territory of the Republic of Tatarstan as our example, in addition to using the results of long-term systematic measurements of informative hydrochemical indicators. In the first stage, typing was performed on surface waters using the neural network clustering method. Clustering was performed based on sets of determined hydrochemical parameters in Kohonen's self-organizing neural network. To assess the uniformity of data, groups in each of the selected clusters were represented by specialists in this subject area's region. To determine the regional threshold values of hydrochemical indicators, statistical data for the corresponding clusters were calculated, and the ranges of these values were used. The results of testing this proposed approach allowed us to recommend it for identifying surface water types, as well as to define the threshold values of hydrochemical indicators in the territory of any region with different surface water compositions.
水体人为负荷的调节是基于水质标准进行的，这些标准是通过水化学指标的阈值来确定的。这些应用标准应在地理上和差异化上进行定义，同时考虑到地表水成分形成的区域特殊性。然而，目前还没有统一的方法来定义这些区域标准。因此，利用现代技术，结合实验数据源和信息系统技术，采用数学方法分析方法，制定区域水质标准是合适的。
根据化学分析集和神经网络聚类分析的使用建议，这两种分析方法和专家评估都可以识别地表水类型，并定义水化学系统指标的官方区域阈值，以提高评估的充分性并确保开发标准的数学合理性。
使用俄罗斯鞑靼斯坦共和国领土上的地表水资源对象作为示例，以及使用长期系统测量的信息水化学指标的结果，对所提出方法的测试过程进行了检验。在第一阶段，使用神经网络聚类方法对地表水进行分类。聚类是基于在科恩神经网络中的确定的水化学参数集进行的。为了评估数据的一致性，在每个选定的聚类中，代表了该主题领域的专家。
为了确定水化学指标的区域阈值，计算了相应聚类的统计数据，并使用了这些值的范围。测试该建议方法的结果使我们能够推荐它用于识别地表水类型，以及定义具有不同地表水成分的任何地区的水化学指标的阈值。
