CAJ | 학술논문

利用误差反相传播神经(BP)网络对河北省近海沉积物中的铅、镉、锌、汞、砷5种重金属元素的污染水平进行分析,利用自组织特征映射(SOFM)网络对上述重金属元素分布特征进行分类,通过分类与污染水平量化值的结合,进行综合评价.SOFM把52个沉积物样品分别划分为3、4、6类和9类.对比各种分类,分为3类的物理意义较明确,每个类别分别对应高中低不同的污染物浓度水平,差异显著、分类方式比较合理.通过此种分类可以判断河北省近海的沉积物重金属污染在不同海域存在一定的差别,整体上是离海岸越远,沉积物的重金属污染水平越高,距海岸较近的海域内,沉积物的重金属污染水平较低,但渤海湾内的重金属污染水平高于其他海域.
이용오차반상전파신경(BP)망락대하북성근해침적물중적연、력、자、홍、신5충중금속원소적오염수평진행분석,이용자조직특정영사(SOFM)망락대상술중금속원소분포특정진행분류,통과분류여오염수평양화치적결합,진행종합평개.SOFM파52개침적물양품분별화분위3、4、6류화9류.대비각충분류,분위3류적물리의의교명학,매개유별분별대응고중저불동적오염물농도수평,차이현저、분류방식비교합리.통과차충분류가이판단하북성근해적침적물중금속오염재불동해역존재일정적차별,정체상시리해안월원,침적물적중금속오염수평월고,거해안교근적해역내,침적물적중금속오염수평교저,단발해만내적중금속오염수평고우기타해역.
By means of two artificial neural networks, SOFM network and BP network, this paper makes a comprehensive assess-ment to five kinds of heavy metal pollution (lead, cadmium, zinc, mercury and arsenic) existing in offshore sediments in Hebei prov-ince. 52 sediment samples are used for evaluation. We set up SOFM network to classify and BP network to grade, respectively train-hag 1 000 times. Thus, pollutions are classified into 3, 4, 6 and 9 classifications. Comparing the classifications of SOFM network and grades of BP network, there are good correlations. Especially, the results of 3 classifications are corresponding to high, middle and low levels of comprehensive pollution, which should be more reasonable. The result also shows the distribution characteristic of heavy metal pollution in offshore sediments: The farther offcoastal line, the more serious the pollution is. Among different sea areas, the Bohai Bay has higher heavy metal pollution than others.