• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1223-1232
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• 2021

In this study, a new analysis method by using a "Discharge Gini Coefficient" is presented to determine the degree of inequality in daily discharge throughout the year. The Discharge Gini Coefficient can be calculated using the area relationship with the cumulative percentage of the daily mode discharge in the ascending order according to the cumulative percentage of the date of occurrence of the daily discharge throughout the year. The Discharge Gini Coefficient is presented as a value between 0 and 1, and the degree of inequality can be divided into 5 levels. The Discharge Gini Coefficient can be used to estimate the discharge stability of the downstream point relative to the upstream point. In addition, it is possible to quantify the influence of each reference discharge on the total inequality. The applicability of the Discharge Gini Coefficient was reviewed using long-term daily discharge data at eight points upstream and downstream of the four major rivers in Korea. The Discharge Gini Coefficient can also be used to analyze the discharge control effect in the downstream by the upstream dam.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.713-724
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• 2013

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.