白城市地下水质量现状评价  

Groundwater Quality Assessment of Baicheng City

作　　者：陈昌亮[1] 肖长来[1] 赵琳琳[1] 张英琦 翟天放[3] 

机构地区：[1]吉林大学环境与资源学院,吉林长春130021 [2]吉林省地质矿产勘察开发局,吉林长春130052 [3]吉林省水利科学研究院,吉林长春130022

出　　处：《人民黄河》2013年第11期38-40,共3页Yellow River

基　　金："十一五"国家科技支撑计划项目(2006BAJ08B09-01;2007BAB28B04-03);高等学校博士学科点专项科研基金资助项目(200801830044);吉林省重大科技攻关项目(20080543;20100452);国家潜在油气资源产学研用合作创新项目(20100331-OSR01-7)

摘　　要：根据吉林省白城市境内选取的9个测点地下水指标的实测数据,对白城市地下水质量现状进行评价。选取的评价指标分别为氨氮、铁、氟化物、铅、砷、高锰酸钾、矿化度、硬度。评价方法分别采用加附注评分法与人工神经网络法,其中人工神经网络法选用BP神经网络、T-S模糊神经网络2种方法。评价结果显示:2种人工神经网络法评价的水质类别均在Ⅰ～Ⅲ类之间,水质较好;加附注评分法评价出的水质类别中只有穆家店屯水质属于Ⅱ类,其他测点均为Ⅳ类。对比3种方法评价的结果可知,BP神经网络与模糊神经网络评价的水质类型之间的差异较小,加附注评分法比其他2种方法评价得出的水质类别大。According to the measured data of nine locations in Baicheng City of Jilin Province,this paper assessed the groundwater quality of cur-rent situation. The selected assessment indexes were ammonia nitrogen,ferrum,fluoride,lead,arsenic,potassium permanganate,total dissoloved solid and hardness,respectively. The adopted assessment methods were Attached Note Scoring Method and Artificial Neural Network,the later in-cluding BP Neural Network and T-S Fuzzy Neural Network. The assessment results show that the groundwater quality of nine locations is good and between gradeⅠand gradeⅢby using two kinds of Artificial Neural Network. By using Attached Note Scoring Method,only the groundwater quali-ty of Mujiadian village is gradeⅡ,and the other locations are gradeⅣ. Comparing the assessment results of three methods,the difference between BP Neural Network and T-S Fuzzy Neural Network is smaller,while the difference of Attached Note Scoring Method is greater than that of the oth-er two methods.

关 键 词：地下水质量评价 加附注评分法 BP神经网络 T-S模糊神经网络 白城市 

分 类 号：X824[环境科学与工程—环境工程]