Neuro-fuzzy method of processing hydrochemical data for river flow

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Production and socio-environmental requirements for the quality of land waters have determined the need to create a network of hydrochemical observation posts, and the variability of controlled indicators – the need to perform routine chemical analytical studies. The standard (rigid) statistical methods of processing measurement results common in analytical chemistry, as a rule, underestimate the specifics of studying noisy (fuzzy) experimental data, which are the series of values of the impurity concentration of a river stream in space and time. It is shown that in this case, alternative soft computing tools designed to process exactly such data based on neuro-fuzzy hybrid algorithmic structures related to the ANFIS architecture are appropriate. The arrays of chemical analytical data on copper and zinc analyzed in this way on the Volga River, depending on water flow at different distances from the shore and depths, made it possible to identify the complex oscillatory behavior of concentrations of both substances in the water stream. It is concluded that the neuro-fuzzy scheme for processing monitoring results provides an opportunity for in-depth study of poorly understood processes of hydrochemical dynamics in systems far from thermodynamic equilibrium, which include natural watercourses.

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作者简介

O. Rosenthal

Institute of Water Problems of the Russian Academy of Sciences

编辑信件的主要联系方式.
Email: omro3@yandex.ru
俄罗斯联邦, 3b, Gubkin St., Moscow, 119333

V. Fedotov

Ulyanov Chuvash State University

Email: omro3@yandex.ru
俄罗斯联邦, 15, Moskovsky Ave., Cheboksary, Chuvash Republic, 428015

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2. Fig. 1. Concentrations in 2021 (excluding the first two months) of copper (enlarged 10 times, dotted lines) and zinc (dashed lines), as well as their linear trends (hereinafter μg/L, dotted lines) at depths indicated in the figure in metres.

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3. Fig. 2. Found concentration and measurement error (vertical segments) of zinc at a depth of 0.5 m in 2021.

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4. Fig. 3. Copper concentration depending on influencing factors: (a) - flow rate 4690 m3/s; (b) - depth 6.80 m; (c) - width 0.5 (proportion of width).

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5. Fig. 4. Copper concentration depending on influencing factors: (a) - depth 5.25 m, flow rate 4690 m3/s; (b) - width 0.5 (width fraction), flow rate 4690 m3/s; (c) - width 0.5 (width fraction), depth 5.25 m.

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6. Fig. 5. Zinc concentration depending on influencing factors: (a) - flow rate 4690 m3/s; (b) - depth 6.80 m; (c) - width 0.5 (proportion of width).

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7. Fig. 6. Zinc concentration depending on influencing factors: (a) - depth 5.25 m, flow rate 4690 m3/s; (b) - width 0.5 (width fraction), flow rate 4690 m3/s; (c) - width 0.5 (width fraction), depth 5.25 m.

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8. Fig. 7. The concentration of copper (lower line) and zinc (upper line) obtained from the forecast results at a distance of 0.8 (fraction of width) from the sampling point relative to the width of the stream (a), at a depth of 10 m (b) and at a water flow rate of 2080 m3/s (c). The y-axis is on a logarithmic scale.

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9. Fig. 8. Dependence of copper concentration (dotted line) on water discharge (solid line) at different depths (dashed line): 0.5 m at measurements No. 1-12, 5 m at 13-25, 10 m at 25-36. The scale of the ordinate axis is logarithmic.

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10. Fig. 9. Concentrations and trends of third-degree copper (dotted lines) and zinc (solid lines) depending on the distance (in fractions of width) to the shore (dashed line): 0.2 for measurements No. 1–12, 0.5 for 13–25, 0.8 for 25–36. The y-axis scale is logarithmic.

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