scholarly journals Curve fitting the hydrodynamic and dispersion characteristics of pollutant released in an inland water system

2020 ◽  
Vol 5 (2) ◽  
pp. 038-046
Author(s):  
Baridakara Nwidadah ◽  
Olalekan Michael Adeloye
Keyword(s):  
Research Study ◽  
Dispersion Coefficient ◽  
Water System ◽  
Tracer Experiment ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Absolute Value ◽  
Constant Distance ◽  
Discrepancy Ratio ◽  
Distance Variable

The research study was performed by estimating the longitudinal dispersion coefficient for Dor Nwezor section of Bodo-Bonny River and conducting a tracer experiment using the constant distance variable time method. Eleven empirical models for the prediction of longitudinal dispersion coefficients were considered and analyzed using the hydraulic and geometric parameters of the river. The empirical and experimental results were analysed and compared statistically with Deng et al model yielded the most reliable method of predicting the longitudinal coefficient of dispersion of Dor Nwezor section of Bodo-Bonny River with the least root mean square value of 0.1221, mean absolute value of 0.0617 close to zero and discrepancy ratio of -0.2303 that falls within the accepted accuracy range of -0.3 to 0.3.

The Tracer Experimental Method for Determining Longitudinal Dispersion Coefficient of Nature River

2013 ◽  
Vol 748 ◽  
pp. 1155-1159 ◽  
Author(s):  
Yong Fan ◽  
Ke Bin Shi
Keyword(s):  
Experimental Method ◽  
Dispersion Coefficient ◽  
Flow Characteristics ◽  
Tracer Experiment ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Local Conditions ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Natural Rivers

Tracer experiment to obtain flow longitudinal dispersion coefficient is the most direct and reliable method. In this paper, the existing natural rivers longitudinal dispersion coefficient tracer experimental method for calculating is analyzed comprehensively. Focuses on the method of moments, the principles and applications of regression analysis, calculus optimization methods, and analyzes their advantages and disadvantages. In recent years the calculus optimization method is outstanding, determine river longitudinal dispersion coefficient method was developed and applied to an instance of some of the new tracer experiment. Local conditions should combine with topographic flow characteristics to select the appropriate calculation methods in practical applications. Finally, some problems of the natural rivers longitudinal dispersion coefficient study that need further investigation were put forward.

scholarly journals Investigation of a parameter estimation method for contaminant transport in aquifers

2001 ◽  
Vol 3 (4) ◽  
pp. 203-213 ◽  
Author(s):  
Channa Rajanayaka ◽  
Don Kulasiri
Keyword(s):  
Experimental Data ◽  
Hydraulic Conductivity ◽  
Contaminant Transport ◽  
Dispersion Coefficient ◽  
Estimation Method ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
System Noise ◽  
Estimated Parameters ◽  
Comparison Of The Results

Real world groundwater aquifers are heterogeneous and system variables are not uniformly distributed across the aquifer. Therefore, in the modelling of the contaminant transport, we need to consider the uncertainty associated with the system. Unny presented a method to describe the system by stochastic differential equations and then to estimate the parameters by using the maximum likelihood approach. In this paper, this method was explored by using artificial and experimental data. First a set of data was used to explore the effect of system noise on estimated parameters. The experimental data was used to compare the estimated parameters with the calibrated results. Estimates obtained from artificial data show reasonable accuracy when the system noise is present. The accuracy of the estimates has an inverse relationship to the noise. Hydraulic conductivity estimates in a one-parameter situation give more accurate results than in a two-parameter situation. The effect of the noise on estimates of the longitudinal dispersion coefficient is less compared to the effect on hydraulic conductivity estimates. Comparison of the results of the experimental dataset shows that estimates of the longitudinal dispersion coefficient are similar to the aquifer calibrated results. However, hydraulic conductivity does not provide a similar level of accuracy. The main advantage of the estimation method presented here is its direct dependence on field observations in the presence of reasonably large noise levels.

A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers

2011 ◽  
pp. 358-374
Author(s):  
Jianhua Yang ◽  
Evor L. Hines ◽  
Ian Guymer ◽  
Daciana D. Iliescu ◽  
Mark S. Leeson ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dispersion Coefficient ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Artificial Neural ◽  
Input Variables ◽  
Training Examples ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes Genetic Algorithms (GAs) to identify variables that are being input into a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), which simplifies the neural network structure and makes the training process more efficient. Once input variables are determined, GNMM processes the data using an MLP with the back-propagation algorithm. The MLP is presented with a series of training examples and the internal weights are adjusted in an attempt to model the input/output relationship. GNMM is able to extract regression rules from the trained neural network. The effectiveness of GNMM is demonstrated by means of case study data, which has previously been explored by other authors using various methods. By comparing the results generated by GNMM to those presented in the literature, the effectiveness of this methodology is demonstrated.

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