Neuro-fuzzy method of processing hydrochemical data for river flow

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Abstract

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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About the authors

O. M. Rosenthal

Institute of Water Problems of the Russian Academy of Sciences

Author for correspondence.
Email: omro3@yandex.ru
Russian Federation, 3b, Gubkin St., Moscow, 119333

V. Kh. Fedotov

Ulyanov Chuvash State University

Email: omro3@yandex.ru
Russian Federation, 15, Moskovsky Ave., Cheboksary, Chuvash Republic, 428015

References

  1. Золотов Ю.А. Основы аналитической химии. Кн. 1. Общие вопросы. М.: Высшая школа, 2002. 351 с.
  2. Tsakovski S.L., Venelinov T. Environmental analytical chemistry // Molecules. 2024. V. 29. № 2. P. 450. https://doi.org/10.3390/molecules29020450.
  3. Конференции “Эконалалитика” // Журн. аналит. химии. 2020. Т. 75. № 9. С. 855. https://doi.org/10.31857/S0044450220090200.
  4. Wilkinson K.J., Lead J.R. Environmental Colloids and Particles: Behaviour, Separation and Characterisation. San Francisco: John Wiley & Sons, 2007. 702 p.
  5. Шевцов М.Н. Водно-экологические проблемы и использование водных ресурсов. Хабаровск: Изд-во Тихоокеан. гос. ун-та, 2015. 197 с.
  6. Kailash B.G., Bisht P.S. The role of water resources in socio-economic development // Int. J. Res. Appl. Sci. Eng. Technol. 2017. V. 5. № 12. P. 1669.
  7. Malov A.I., Sidkina E.S., Ershova D.D., Cherkasova E.V., Druzhinin S.V. Time regularities of strontium concentration in drinking groundwater distant from the sea coast // Environ. Geochem. Health. 2023. V. 45. № 11. P. 8097. https://doi.org/10.1007/s10653-023-01710-9
  8. Wilcox В.Р., Seyfried M.S., Matison Т.Н. Searching for chaotic dynamics in Snowmelt runoff // Water Resour. 1991. V. 27. № 6. P. 1005. https://doi.org/10.1029/91WR00225.
  9. Швейкина В.И., Кожевникова И.А. Нелинейная модель колебаний речного стока с хаотическими режимами // Водное хозяйство России: проблемы, технологии, управление. 2012. № 6. С. 4.
  10. РД 52.24.634-2002. Руководящий документ. Методические указания. Уточнение местоположения створов (пунктов) наблюдений и режимов отбора проб на основе использования трассерных методов изучения гидродинамических характеристик водных объектов (утв. и введен в действие Росгидрометом 16.05.2002). 20 с.
  11. Розенталь О.М., Александровская Л.Н. Оценка степени соответствия воды нормативным требованиям // Водные ресурсы. 2018. Т. 45. № 3. С. 289. https://doi.org/10.7868/S0321059618030070 (Rosental O.M., Aleksandrovskaya L.N. Assessment of the degree of compliance of water to regulatory requirements // Water Resour. 2018. V. 45. № 3. P. 379. https://doi.org/10.1134/S0097807818030132).
  12. Thomas L., Ferrari R. Friction, frontogenesis, and the stratification of the surface mixed layer // J. Phys. Oceanogr. 2008. V. 38. № 38. P. 2501.
  13. Чашечкин Ю.Д., Розенталь О.М. Структура речного потока и ее влияние на распределение загрязняющего воду вещества // Водные ресурсы. 2019. Т. 46. № 6. С. 582. https://doi.org/10.31857/S0321-0596466582-591 (Chashechkin Yu.D., Rozental O.M. River flow structure and its effect on pollutant distribution // Water Resour. 2019. V. 46. № 6. P. 910. https://doi.org/10.1134/S0097807819060022).
  14. РД 52.24.309-2011. Организация и проведение режимных наблюдений за состоянием и загрязнением поверхностных вод суши. Ростов-на-Дону, 2011. 109 с.
  15. ГОСТ 27384-2002. Вода. Нормы погрешности измерений показателей состава и свойств. М.: Стандартинформ, 2010.
  16. Розенталь О.М., Авербух А.И. Введение в квалиметрию воды // Водные ресурсы. 2013. Т. 40. № 4. С. 418. https://doi.org/10.7868/S0321059613040111 (Rozental O.M., Averbukh A.I. Introduction to water qualimetry // Water Resour. 2013. V. 40. P. 447. https://doi.org/10.1134/S0097807813040118)
  17. Кудинов Ю.И., Келина А.Ю., Кудинов И.Ю., Пащенко А.Ф., Пащенко Ф.Ф. Нечеткие модели и системы управления. М.: ЛЕНАНД, 2017. 328 с.
  18. Федотов В.Х. Мягкое описание фундаментальных законов природы и общества для экспертных систем // European Researcher (Европейский исследователь). 2012. № 2 (17). С. 110. EDN: OUINYP.
  19. Терано Т., Асаи К., Сугэно М. Прикладные нечеткие системы. М.: Мир, 1993. 368 p.
  20. Штовба С.Д. Проектирование нечетких систем средствами MATLAB. М.: Горячая линия-Телеком, 2007. 288 с.
  21. Леонов А.С. Решение некорректно поставленных обратных задач: очерк теории, практические алгоритмы и демонстрации в МАТЛАБ. М.: Либроком, 2015. 336 с.
  22. Kuhn K.M., Neubauer E., Hofmann T., von der Kammer F., Aiken G.R., Maurice P.A. Concentrations and distributions of metals associated with dissolved organic matter from the Suwannee River // Environ. Eng. Sci. 2015. V. 32. № 1. P. 54. https://doi.org/10.1089/ees.2014.0298
  23. Danilov-Danilyan V.I., Rosenthal O.M. Dynamic model of water quality evolution // J. Water Chem. Technol. 2022. V. 44. № 2. P. 132. https://doi.org/10.3103/S1063455X22020035

Supplementary files

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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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