BP人工神经网络模型在珠江口水质评价中的应用

根据海水水质标准GB30971997,应用VB编程语言在各类海水水质指标浓度区间内生成足够多的随机分布样本,以此作为海水水质评价BP人工神经网络模型的训练、检验和测试样本,利用训练后的海水水质评价BP人工神经网络模型对珠江口2002～2003年水质状况做出评价。结果表明,训练后的海水水质评价BP人工神经网络模型具有较好的泛化能力,能够准确评价未知海水样本的水质类别;2002～2003年珠江口的水环境总体状况较差,绝大部分区域属于II～IV类海水,在其分布上,珠江口西部海域水质状况好于东部海域,这是由于珠江口东部沿岸城市东莞和深圳较大的排污量和繁忙的海上运输所引起。

The application of BP artificial neural networks on assessment of seawater quality at Pearl River Estuary

According standard of seawater quality GB3097-1997, plenty of random samples in the concentration scale of different water quality kinds were produced by VB program, then they were used to train and test the artificial neural networks model based on BP arithmetic, and the seawater quality of Pearl River Estuary in 2002 and 2003 were assessed by the BP artificial neural networks model which had been trained. The results showed that the BP artificial neural networks model which had been trained could estimate the seawater quality kinds of unknown seawater samples exactly, and the seawater quality of Pearl River Estuary in 2002 and 2003 were bad, from class II to class IV in most area. On the distribution of the seawater quality in Pearl River Estuary, the western was better than the eastern what was caused by (1) the large amount polluted water discharged from Dongguan and Shenzhen city located at the east seacoast of Pearl River Estuary; (2) the busy sea transportation in east area of Pearl River Estuary.