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首页> 外文期刊>Journal of Environmental Science and Health. A, Toxic/Hazardous Substances & Environmental Engineering >Convective Oxygen Transport in a Constructed Wetland Pond: Mechanism, Measurements and Modelling by Multilayer Perceptrons
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Convective Oxygen Transport in a Constructed Wetland Pond: Mechanism, Measurements and Modelling by Multilayer Perceptrons

机译:人工湿地池塘中的对流氧气传输:多层感知器的机理,测量和建模

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摘要

The study presented here found evidence for the presence and importance of convec-tive flows and associated oxygen transport in a constructed wetland pond in southern Finland. These flows are triggered by nightly cooling of the water at the surface, which may then become denser than the water lower down in the pond. The resulting layering (heavier water overlying less dense one) is hydromechanically unstable and—almost immediately—starts driving convective motion. This flow takes oxygen enriched water from the surface to the bottom of the wetland, where a distinct rise in oxygen saturation is recorded after some time lag. The process described can be modelled successfully by means of so-called Multilayer Perceptrons (MLPs), a class of Artificial Neural Networks. As explored in this study, these models are well suited to "learn" the mechanism of convective transport, which results in their ability to forecast oxygen saturation near the wetland bottom at a satisfactory level of accuracy.
机译:此处提出的研究发现了芬兰南部人工湿地池塘中对流和相关氧气输送的存在和重要性的证据。这些水流是由夜间表层水的夜间冷却触发的,然后水的密度可能会比池塘中较低的水高。所产生的分层(较重的水覆盖着较稀疏的层)在流体力学上是不稳定的,并且几乎立即开始驱动对流运动。这种流动将富氧水从湿地的表面带到了湿地的底部,经过一段时间后记录到那里的氧饱和度明显上升。可以通过所谓的多层感知器(MLP)(一类人工神经网络)成功地对所描述的过程进行建模。正如本研究中探讨的那样,这些模型非常适合“学习”对流传输的机制,从而使它们能够以令人满意的准确度预测湿地底部附近的氧饱和度。

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