%0 Journal Article %1 chalmers1998order %A Rozental, O. M. %D 2023 %I BOHR Publishers %J BOHR International Journal of Civil Engineering and Environmental Science %K analysis and artificial correlations discharges industrial multiple networks neural neural-network pair quality site waste water water-monitoring %N 2 %P 80-86 %R 10.54646/bijcees.2023.10 %T Structural water self-organization in river flow %V 1 %X Application of neural-network methods revealed relationships between hydrological and hydro chemical characteristics of water flow, suggesting structural self-organization of substances dissolved in water in the form of micro layering. In particular, the coefficient of correlation between the concentrations of such substances in some cases reaches its nearly maximal value (0.99), combining with the high weights of neural network edges. This can be supposed to be due to the mechanical and chemical interactions in river flow with the participation of Van der Waals forces, hydration, and sorption. Other factors, not taken into account, can also have their effect, in particular those responsible for the fluctuations of the parameters of order, determining the singular contributions to the dynamic characteristics of the non-linear system under consideration. Such can be the cyclic oscillations of the characteristics under control with an amplitude decreasing with a decrease in the intensity of the pollution/self purification processes in water medium and increasing with an increase in this intensity. The obtained information, in addition to its direct purpose as a means to study the nature and properties of fresh water, is a necessary condition for the effective control of water resource quality and water management activity.

@article{chalmers1998order, abstract = {Application of neural-network methods revealed relationships between hydrological and hydro chemical characteristics of water flow, suggesting structural self-organization of substances dissolved in water in the form of micro layering. In particular, the coefficient of correlation between the concentrations of such substances in some cases reaches its nearly maximal value (0.99), combining with the high weights of neural network edges. This can be supposed to be due to the mechanical and chemical interactions in river flow with the participation of Van der Waals forces, hydration, and sorption. Other factors, not taken into account, can also have their effect, in particular those responsible for the fluctuations of the parameters of order, determining the singular contributions to the dynamic characteristics of the non-linear system under consideration. Such can be the cyclic oscillations of the characteristics under control with an amplitude decreasing with a decrease in the intensity of the pollution/self purification processes in water medium and increasing with an increase in this intensity. The obtained information, in addition to its direct purpose as a means to study the nature and properties of fresh water, is a necessary condition for the effective control of water resource quality and water management activity.}, added-at = {2024-06-12T09:27:34.000+0200}, author = {Rozental, O. M.}, biburl = {https://www.bibsonomy.org/bibtex/2713d62cc4ab0a9d08ccbc9aae9474ee4/bijceesjournal}, doi = {10.54646/bijcees.2023.10}, interhash = {b78383cea34de491fae5e7d077dba11c}, intrahash = {713d62cc4ab0a9d08ccbc9aae9474ee4}, issn = {2583-8857}, journal = {BOHR International Journal of Civil Engineering and Environmental Science}, keywords = {analysis and artificial correlations discharges industrial multiple networks neural neural-network pair quality site waste water water-monitoring}, language = {English}, number = 2, pages = {80-86}, publisher = {BOHR Publishers}, timestamp = {2024-06-12T09:27:34.000+0200}, title = {Structural water self-organization in river flow}, volume = 1, year = 2023 }