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首页> 外文会议>Congress of Asia and Pacific Division of International Association of Hydraulic Engineering and Research >NEW APPROACH FOR ESTIMATION OF POLLUTANT LOAD BY USING ARTIFICIAL NEURAL NETWORK
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NEW APPROACH FOR ESTIMATION OF POLLUTANT LOAD BY USING ARTIFICIAL NEURAL NETWORK

机译：用人工神经网络估计污染物负荷的新方法

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Pollutant load transported by rivers from non-point sources in watersheds is the major cause of eutrophication of closed water bodies such as lakes, reservoirs, and inner bays. Because it concentrates in a short time of flood event, regular water sampling cannot be adequate to monitor it. This study proposes a new type of monitoring technique of pollutant load in rivers: Optical characteristics of river water are monitored by a multi-item optical device. The relation between the sensor signals and the water qualities obtained from occasional sample analysis is modeled by Artificial Neural Network (ANN). After then, the time series of pollutant load can be produced from the optical signals. Field experiments were conducted in seven rivers flowing into Lake Kasumigaura. The ANN model trained by the data obtained in the year 2005 successfully produced the time series of pollutant load observed in the years 2006 and 2007. ANN models worked well in watersheds of different land use conditions if it was trained by the data obtained in each river.
机译：来自流域的非点源的河流运输的污染物负荷是湖泊，水库和内海湾等封闭水体富营养化的主要原因。因为它专注于洪水事件的短时间，所以常规的水采样不能足以监测它。本研究提出了一种新型的河流污染物负荷监测技术：通过多项光学装置监测河水的光学特性。从偶尔样本分析中获得的传感器信号和水质之间的关系是由人工神经网络（ANN）建模的。然后，可以从光信号产生污染物负载的时间序列。在七个流入Kasumigaura的七个河流中进行了现场实验。由2005年获得的数据培训的ANN模型成功地制作了2006年和2007年观察到的时间污染物负荷。如果通过在每条河流中获得的数据训练，ANN模型在不同的土地使用条件的流域中工作得很好。

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