College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450001, China.
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
Int J Environ Res Public Health. 2023 Feb 25;20(5):4148. doi: 10.3390/ijerph20054148.
In order to develop a better model for quantifying aquatic community using environmental factors that are easy to get, we construct quantitative aquatic community models that utilize the different relationships between water environmental impact factors and aquatic biodiversity as follows: a multi-factor linear-based (MLE) model and a black box-based 'Genetic algorithm-BP artificial neural networks' (GA-BP) model. A comparison of the model efficiency and their outputs is conducted by applying the models to real-life cases, referring to the 49 groups of seasonal data observed over seven field sampling campaigns in Shaying River, China, and then performing model to reproduce the seasonal and inter-annual variation of the water ecological characteristics in the Huaidian (HD) site over 10 years. The results show that (1) the MLE and GA-BP models constructed in this paper are effective in quantifying aquatic communities in dam-controlled rivers; and (2) the performance of GA-BP models based on black-box relationships in predicting the aquatic community is better, more stable, and reliable; (3) reproducing the seasonal and inter-annual aquatic biodiversity in the HD site of Shaying River shows that the seasonal variation of species diversity for phytoplankton, zooplankton, and zoobenthos are inconsistent, and the inter-annual levels of diversity are low due to the negative impact of dam control. Our models can be used as a tool for aquatic community prediction and can become a contribution to showing how quantitative models in other dam-controlled rivers to assisting in dam management strategies.
为了开发更好的模型来量化容易获得的环境因素对水生群落的影响，我们构建了利用水环境保护影响因素与水生生物多样性之间不同关系的定量水生群落模型，如下所示：多因素线性模型（MLE）和基于黑箱的“遗传算法-反向传播人工神经网络”（GA-BP）模型。通过将模型应用于实际案例，比较模型效率及其输出结果，我们参考了中国沙颍河 7 次野外采样活动中观察到的 49 组季节性数据，并使用模型重现了淮淀（HD）站点 10 年来的水生态特征的季节性和年际变化。结果表明：（1）本文构建的 MLE 和 GA-BP 模型可有效量化受大坝控制的河流中的水生群落；（2）基于黑箱关系构建的 GA-BP 模型在预测水生群落方面的性能更好、更稳定、更可靠；（3）重现沙颍河 HD 站点的季节性和年际水生生物多样性表明，浮游植物、浮游动物和底栖动物的物种多样性季节性变化不一致，由于大坝控制的负面影响，多样性的年际水平较低。我们的模型可以用作水生群落预测的工具，并有助于展示其他受大坝控制的河流中的定量模型如何协助大坝管理策略。
