Paper/Poster/Presentation Title

Information flow in the river

Keywords

information entropy,pollutant transport, diffusion, river, network design

Start Date

25-6-2018 9:00 AM

End Date

25-6-2018 10:20 AM

Abstract

Information entropy theory, firstly advanced by Claude Shannon in 1948, has been widely used in many science and engineering fields. Although a few of researchers have studied the process of solute transport in surface water and groundwater system using entropy (H) theory, the comprehensive explanation and analysis of how information ‘flow’ in the river is not concluded and reported. In this work, we analyze the temporal and spatial change of information entropy in the process of solute transport under one-dimensional generalizability. We also defined the critical points in time and space of information entropy evolution, where H of pollutant plume is the largest, then explored the physical meaning of it. The change of controlling parameters of water quality model (diffusion coefficient Dx, flow speed u, degradation coefficient K, and source term M) directly lead to attenuation of entropy H and spatial information transport index (SITI). We analyze the qualitative and quantitative relationship between information entropy, SITI and flow speed, diffusion coefficient and degradation coefficient. According to the results, there is a positive correlation between information entropy and diffusion coefficient, a negative correlation between information entropy and degradation coefficient, and there is no correlation with degradation coefficient. For the SITI, the larger diffusion coefficient, flow speed and degradation coefficient is, the faster the ITI decreases. The smaller diffusion coefficient, flow speed and degradation coefficient is, the higher the efficiency of information transport is. The combination of entropy theory and water quality modelling provide a useful tool for optimization of water quality monitoring network and a new perspective on characterizing the pollutants transport and parameter uncertainty quantification.

Stream and Session

D1: Environmental Fluid Mechanics – Theoretical, Modelling, and Experimental Approaches

OR

B2: Hybrid modelling and innovative data analysis for integrated environmental decision support


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Jun 25th, 9:00 AM Jun 25th, 10:20 AM

Information flow in the river

Information entropy theory, firstly advanced by Claude Shannon in 1948, has been widely used in many science and engineering fields. Although a few of researchers have studied the process of solute transport in surface water and groundwater system using entropy (H) theory, the comprehensive explanation and analysis of how information ‘flow’ in the river is not concluded and reported. In this work, we analyze the temporal and spatial change of information entropy in the process of solute transport under one-dimensional generalizability. We also defined the critical points in time and space of information entropy evolution, where H of pollutant plume is the largest, then explored the physical meaning of it. The change of controlling parameters of water quality model (diffusion coefficient Dx, flow speed u, degradation coefficient K, and source term M) directly lead to attenuation of entropy H and spatial information transport index (SITI). We analyze the qualitative and quantitative relationship between information entropy, SITI and flow speed, diffusion coefficient and degradation coefficient. According to the results, there is a positive correlation between information entropy and diffusion coefficient, a negative correlation between information entropy and degradation coefficient, and there is no correlation with degradation coefficient. For the SITI, the larger diffusion coefficient, flow speed and degradation coefficient is, the faster the ITI decreases. The smaller diffusion coefficient, flow speed and degradation coefficient is, the higher the efficiency of information transport is. The combination of entropy theory and water quality modelling provide a useful tool for optimization of water quality monitoring network and a new perspective on characterizing the pollutants transport and parameter uncertainty quantification.