Department of Earth Sciences, University of Minnesota, Minneapolis, MN, 55455, USA.
Institute of Engineering Research, Seoul National University, Seoul, 08826, South Korea.
Sci Rep. 2019 Jun 25;9(1):9266. doi: 10.1038/s41598-019-45621-1.
This study investigates a seasonally varying response of phytoplankton biomass to environmental factors in rivers. Artificial neural network (ANN) models incorporated with a clustering technique, the clustered ANN models, were employed to analyze the relationship between chlorophyll a (Chl-a) and the explanatory variables in the regulated Nakdong River, South Korea. The results show that weir discharge (Q) and total phosphorus (TP) were the most influential factors on temporal dynamics of Chl-a. The relative importance of both variables increased up to higher than 30% for low water temperature seasons with dominance of diatoms. While, during summer when cyanobacteria predominated, the significance of Q increased up to 45%, while that of TP declined to about 10%. These tendencies highlight that the effects of the river environmental factors on phytoplankton abundance was temporally inhomogeneous. In harmful algal bloom mitigation scenarios, the clustered ANN models reveals that the optimal weir discharge was 400 m/s which was 67% of the value derived from the non-clustered ANN models. At the immediate downstream of confluence of the Kumho River, the optimal weir discharge should increase up to about 1.5 times because of the increase in the tributary pollutant loads attributed to electrical conductivity (EC).
本研究调查了浮游植物生物量对河流环境因素的季节性变化响应。人工神经网络 (ANN) 模型结合聚类技术，即聚类 ANN 模型，用于分析韩国调节后的纳东江中叶绿素 a (Chl-a) 与解释变量之间的关系。结果表明，堰坝流量 (Q) 和总磷 (TP) 是 Chl-a 时间动态变化的最主要影响因素。这两个变量的相对重要性在硅藻占主导地位的低温季节增加到 30%以上。而在以蓝藻为主的夏季，Q 的重要性增加到 45%，而 TP 的重要性则下降到约 10%。这些趋势表明，河流环境因素对浮游植物丰度的影响在时间上是不均匀的。在有害藻类水华缓解情景中，聚类 ANN 模型揭示了最优堰坝流量为 400 立方米/秒，这是从非聚类 ANN 模型得出的值的 67%。在 Kumho 河汇流的下游，由于电导率 (EC) 导致的支流污染物负荷增加，最优堰坝流量应增加到约 1.5 倍。
