The Use of Neural Network Modeling Methods to Determine Regional Threshold Values of Hydrochemical Indicators in the Environmental Monitoring System of Waterbodies

Yulia Tunakova; Svetlana Novikova; Vsevolod Valiev; Evgenia Baibakova; Ksenia Novikova

doi:10.3390/s23136160

The Use of Neural Network Modeling Methods to Determine Regional Threshold Values of Hydrochemical Indicators in the Environmental Monitoring System of Waterbodies

Sensors (Basel). 2023 Jul 5;23(13):6160. doi: 10.3390/s23136160.

Authors

Yulia Tunakova¹, Svetlana Novikova², Vsevolod Valiev³, Evgenia Baibakova¹, Ksenia Novikova¹

Affiliations

¹ Department of General Chemistry and Ecology, Kazan National Research Technical University Named after A.N. Tupolev-KAI, 10 K. Marx St., Kazan 420111, Russia.
² 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.
³ Research Institute for Problems of Ecology and Mineral Wealth Use of Tatarstan Academy of Sciences, 28 Daurskaya St., Kazan 420087, Russia.

Abstract

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.

Keywords: environmental monitoring; hydrochemical indicators; neural network technologies; regional research project standards; surface water.

MeSH terms

Cluster Analysis
Environmental Monitoring / methods
Water Pollutants, Chemical* / analysis
Water Quality

Substances

Water Pollutants, Chemical

Grants and funding

Scientific research was carried out with the financial support of the Ministry of Education and Science of the Russian Federation as part of the fulfillment of obligations under the Agreement No. 075-03-2023-032 dated 16 January 2023 (topic number FZSU-2023-0005).