Kuo Jan-Tai, Wang Ying-Yi, Lung Wu-Seng
Department of Civil Engineering, National Taiwan University, Taipei 106, Taiwan.
Water Res. 2006 Apr;40(7):1367-76. doi: 10.1016/j.watres.2006.01.046. Epub 2006 Mar 20.
A combined neural network and genetic algorithm (GA) was developed for water quality management of Feitsui Reservoir in Taiwan. First, an artificial neural network (ANN) model was employed to simulate the behavior of nutrient loads into the reservoir. The data from watershed loads, precipitation in the watershed, and outflow were used in the ANN model to forecast the total phosphorus concentration in the reservoir. A 6-year (1992-97) record of water quality data was used for network training, and additional data collected in 1998-2000 were used for model verification. Further, a GA was used with this ANN model to optimize the control of nutrient loads from the watershed. The GA was used as a search strategy to determine the proper reduction rates of nutrient loads from the watershed so that the objective function could be as close to the optimal value as possible. The study results indicate that the ANN model can effectively simulate the dynamics of reservoir water quality. The GA is able to identify control schemes that reduce the in-reservoir total phosphorus concentration by as much as 60%, and water quality in the reservoir can be expected to achieve an oligotrophic (most of the time) or mesotrophic level if the watershed nutrient loads are reduced by 10-80%.
为了对台湾翡翠水库的水质进行管理，开发了一种结合神经网络和遗传算法（GA）的方法。首先，采用人工神经网络（ANN）模型来模拟进入水库的营养负荷行为。流域负荷、流域降水量和流出量的数据被用于ANN模型，以预测水库中的总磷浓度。利用1992 - 1997年的6年水质数据记录进行网络训练，并将1998 - 2000年收集的额外数据用于模型验证。此外，将遗传算法与该ANN模型结合使用，以优化对流域营养负荷的控制。遗传算法被用作一种搜索策略，以确定流域营养负荷的适当削减率，从而使目标函数尽可能接近最优值。研究结果表明，ANN模型能够有效地模拟水库水质动态。遗传算法能够识别出可将水库中总磷浓度降低多达60%的控制方案，并且如果将流域营养负荷降低10% - 80%，预计水库水质能够达到贫营养（大部分时间）或中营养水平。
