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GA-optimized model predicts dispersion coefficient in natural channels

机译：GA优化模型可预测自然通道中的弥散系数

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摘要

Models whose parameters were optimized by genetic algorithm (GA) were developed to predict the longitudinal dispersion coefficient in natural channels. Following the existing equations in the literature, ten different linear and nonlinear models were first constructed. The models relate the dispersion coefficient to flow and channel characteristics. The GA model was then employed to find the optimal values of the constructed model parameters by minimizing the mean absolute error function (objective function). The GA model utilized an 80% cross-over rate and 4% mutation rate. It started each computation with a population of 100 chromosomes in the gene pool. For each model, while minimizing the objective function, the values of the model parameters were constrained between [-10, +10] at each iteration. The optimal values of the model parameters were obtained using a calibration set of 54 out of 80 sets of measured data. The minimum error was obtained for the case where the model was a linear equation relating dispersion coefficient to flow discharge. The model performance was then satisfactorily tested against the remaining 26 measured validation datasets. It performed better than the existing equations. It yielded minimum errors of MAE = 21.4m~2/s (mean absolute error) and RMSE = 28.5 m~2/s (root mean-squares error) and a maximum accuracy rate of 81%.
机译：开发了通过遗传算法（GA）优化参数的模型，以预测自然通道中的纵向弥散系数。遵循文献中现有的方程式，首先构建了十种不同的线性和非线性模型。这些模型将扩散系数与流量和通道特性联系起来。然后，通过最小化平均绝对误差函数（目标函数），将GA模型用于找到所构建模型参数的最佳值。 GA模型利用了80％的交叉率和4％的突变率。它从基因库中的100条染色体开始进行每次计算。对于每个模型，在最小化目标函数的同时，每次迭代时将模型参数的值限制在[-10，+10]之间。使用80组测量数据中的54组校准集获得模型参数的最佳值。对于该模型是将弥散系数与流量排放相关联的线性方程式的情况，获得了最小误差。然后针对剩余的26个测得的验证数据集对模型性能进行了令人满意的测试。它的性能比现有方程更好。它产生的最小误差为MAE = 21.4m〜2 / s（平均绝对误差）和RMSE = 28.5 m〜2 / s（均方根误差），最大准确率为81％。

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