Department of Health and Environmental Hygiene, Kwandong University, Gangwon-Do, South Korea.
Sci Total Environ. 2010 Mar 15;408(8):1985-91. doi: 10.1016/j.scitotenv.2010.01.025. Epub 2010 Feb 16.
An influence coefficient algorithm and a genetic algorithm (GA) were introduced to develop an automatic calibration model for QUAL2K, the latest version of the QUAL2E river and stream water-quality model. The influence coefficient algorithm was used for the parameter optimization in unsteady state, open channel flow. The GA, used in solving the optimization problem, is very simple and comprehensible yet still applicable to any complicated mathematical problem, where it can find the global-optimum solution quickly and effectively. The previously established model QUAL2Kw was used for the automatic calibration of the QUAL2K. The parameter-optimization method using the influence coefficient and genetic algorithm (POMIG) developed in this study and QUAL2Kw were each applied to the Gangneung Namdaecheon River, which has multiple reaches, and the results of the two models were compared. In the modeling, the river reach was divided into two parts based on considerations of the water quality and hydraulic characteristics. The calibration results by POMIG showed a good correspondence between the calculated and observed values for most of water-quality variables. In the application of POMIG and QUAL2Kw, relatively large errors were generated between the observed and predicted values in the case of the dissolved oxygen (DO) and chlorophyll-a (Chl-a) in the lowest part of the river; therefore, two weighting factors (1 and 5) were applied for DO and Chl-a in the lower river. The sums of the errors for DO and Chl-a with a weighting factor of 5 were slightly lower compared with the application of a factor of 1. However, with a weighting factor of 5 the sums of errors for other water-quality variables were slightly increased in comparison to the case with a factor of 1. Generally, the results of the POMIG were slightly better than those of the QUAL2Kw.
引入影响系数算法和遗传算法（GA），为 QUAL2K 开发自动校准模型，QUAL2K 是 QUAL2E 河流和溪流水质模型的最新版本。影响系数算法用于非稳态、明渠流的参数优化。遗传算法（GA）用于解决优化问题，非常简单和易于理解，但仍然适用于任何复杂的数学问题，可以快速有效地找到全局最优解。先前建立的 QUAL2Kw 模型用于 QUAL2K 的自动校准。本研究中开发的使用影响系数和遗传算法（POMIG）的参数优化方法和 QUAL2Kw 分别应用于具有多个河段的江陵南岱川河，比较了两个模型的结果。在建模过程中，根据水质和水力特征将河段分为两部分。POMIG 的校准结果显示，对于大多数水质变量，计算值与观测值之间具有良好的一致性。在 POMIG 和 QUAL2Kw 的应用中，在河流下游，溶解氧（DO）和叶绿素-a（Chl-a）的观测值和预测值之间产生了相对较大的误差；因此，对下游的 DO 和 Chl-a 应用了两个权重因子（1 和 5）。与应用因子 1 相比，应用因子 5 时 DO 和 Chl-a 的误差总和略低。然而，与应用因子 1 相比，应用因子 5 时其他水质变量的误差总和略有增加。通常，POMIG 的结果略优于 QUAL2Kw。
