[1]卞建民,胡昱欣,李育松,等.基于BP神经网络的辽河源头区水质评价研究[J].水土保持研究,2014,21(01):147-151.
　BIAN Jian-min,HU Yu-xin,LI Yu-song,et al.Water Quality Assessment in Source Area of Liao River Based on BP Neural Network[J].Research of Soil and Water Conservation,2014,21(01):147-151.
点击复制

基于BP神经网络的辽河源头区水质评价研究

《水土保持研究》[ISSN:1005-3409/CN:61-1272/P] 卷: 21 期数: 2014年01期 页码: 147-151 栏目: 出版日期: 2014-02-28
Title:
Water Quality Assessment in Source Area of Liao River Based on BP Neural Network
作者:
卞建民1, 胡昱欣1, 李育松1, 马永祥2, 边静3
1. 吉林大学 环境与资源学院, 长春 130021;
2. 宁夏回族自治区煤田地质局, 银川 750004;
3. 吉林省地质环境监测总站, 长春 130021
Author(s):
BIAN Jian-min1, HU Yu-xin1, LI Yu-song1, MA Yong-xiang2, BIAN Jing3
1. College of Environment and Resources, Jilin University, Changchun 130021, China;
2. Ningxia Hui Autonomous Region Bureau of Coal Geological Exploration, Yinchuan 750004, China;
3. Geo-Environmental Monitoring Central Station of Jilin Province, Changchun 130021, China
关键词:
辽河源头区; BP神经网络; 网络训练; 水质评价
Keywords:
source area of Liao River; BP neural network; network training; water quality assessment
分类号:
X524;TP183
摘要:
基于辽河源头区水环境问题日益突出的现状，该文开展了辽河源头区水环境质量的研究，旨在对区内的水体质量进行分析评价。通过资料收集与汇总，基于BP人工神经网络结构的思想和理论，利用研究区内13个控制断面的水质监测数据，建立了包括pH、溶解氧、氨氮、化学需氧量、五日生化需氧量、高锰酸盐指数的水质综合评价模型，并应用训练好的模型进行仿真运算及水质综合评价。结果显示，在选取的13个断面中，约76.92%的断面为Ⅴ类—劣Ⅴ类水质，仅有23.08%的断面水质级别在Ⅱ—Ⅲ类之间，研究区上游断面的水质状况较好，中下游的水质较差。将该结果与《环境公报》公布的主要断面水质结果进行对比，81.25%的评价结果相同，采用BP神经网络对研究区水质进行综合评价具有较强的适用性和可靠性。
Abstract:
Based on the present situation that the water environmental issues in source area of Liao River have become increasingly prominent, the research for water environment quality was carried out to evaluate and analyze the regional water quality. Through data collection and summary, a comprehensive water quality evaluation model was established based on the thought and theory of BP artificial neural network with including pH, DO, ammonia nitrogen, COD, BOD5, potassium permanganate index, and finished with water quality monitoring data of the 13 sections in the study area. After training well, it can just be applied in model simulating operation and water quality comprehensive evaluation. The results have showed that in the selected sections, approximately 76.92% of the sections are between class V and worse than class V, leaving only 23.08% of the sections whose water quality levels are between class Ⅱand class Ⅲ in the selected 13 sections. The sections located in the upper reaches have a better water quality than that in the downstream. Compared the evaluation results with the results of main sections published in Environment Communique, 81.25% of the evaluation results are identical. It has strong applicability and reliability that BP neural network was used to comprehensively evaluate water quality in the study area.

参考文献/References:

[1] 张利静,余麟,刘红琴,等.辽河源头区跨界污染输入响应模型的建立[J].科学技术与工程,2012,12(23):5952-5955.
[2] 葛淑芳.辽河吉林省段水环境容量研究[D].长春:吉林大学,2012.
[3] 马溪平,吕晓飞,张利红,等.辽河流域水质现状评价及其污染源解析[J].水资源保护,2011,27(4):1-4.
[4] 孙维.基于GIS的辽河源头区地表水评价研究[D].长春:吉林大学,2011.
[5] 郭华.不同评价法在清河水质评价中的应用[J].人民长江,2012,43(1):132-134.
[6] 鲁斐,李磊.主成分分析法在辽河水质评价中的应用[J].水利科技与经济,2006,12(10):660-662.
[7] 侯淑艳,曲晓晶,刘建新,等.基于综合评价法的天津市主要排污河流水质评价[J].水土保持研究,2011,18(4):61-64.
[8] 周林飞,赵崭,孙佳竹,等.基于模糊模式识别的浑河水质评价研究[J].水土保持研究,2012,19(5):163-166.
[9] 刘登峰,齐实,韩小杰,等.缙云山不同土地利用类型地表径流水质评价[J].水土保持研究,2009,16(1):126-130.
[10] 郭庆春,何振芳,李力,等.BP神经网络在渭河水环境质量评价中的应用[J].水土保持通报,2011,31(4):112-115.
[11] 张丽,吴金亮,杨国范.基于BP神经网络的东港灌区作物需水量预测研究[J].水土保持研究,2012,19(6):207-211.
[12] 许秀英,黄操军,杨秋梦,等.基于遗传神经网络的黑龙江浅表地层水分预测[J].水土保持研究,2013,20(1):269-272.
[13] 杜富芝,傅瓦利,杜小红,等.基于BP神经网络的三峡库区小流域水质评价[J].节水灌溉,2009(1):8-10.
[14] 陈东彦.数学建模[M].北京:科学出版社,2007.
[15] 杨芳,原松.基于BP神经网络的水环境质量评价模型的研建[J].人民长江,2008,39(23):46-48.
[16] 马莉.MATLAB数学实验与建模[M].北京:清华大学出版社,2010.
[17] 杨洁,吴贻名,万飚,等.汉江水质综合评价的BP网络方法[J].武汉大学学报:工学版,2004,37(1):51-54.
[18] 王李,刘志斌,常欢.水质评价的BP神经网络应用[C]//第三届全国农业环境科学学术研讨会论文集.2009.
[19] 王庚,王敏生.现代数学建模方法[M].北京:科学出版社,2008.
[20] 楼文高,王延政.基于BP网络的水质综合评价模型及其应用[J].环境污染治理技术与设备,2003,4(8):23-26.

相似文献/References:

[1]张丽,吴金亮,杨国范.基于BP神经网络的东港灌区作物需水量预测研究[J].水土保持研究,2012,19(06):207.
　ZHANG Li,WU Jin-liang,YANG Guo-fan.Prediction Research for Crop Water Requirement Based on BP Neural Network in Donggang Irrigation District[J].Research of Soil and Water Conservation,2012,19(01):207.
[2]封毅,武博强,崔灵周.基于BP神经网络的台风降雨量预测研究[J].水土保持研究,2012,19(03):289.
　FENG Yi,WU Bo-qiang,CUI Ling-zhou.Study on Forecasting Typhoon Rainfall Based on BP Neural Network[J].Research of Soil and Water Conservation,2012,19(01):289.
[3]刘吉平,刘佳鑫,于洋,等.不同采样尺度下土壤碱解氮空间变异性研究——以榆树市农田土壤为例[J].水土保持研究,2012,19(02):106.
　LIU Ji-ping,LIU Jia-xin,YU Yang,et al.Study on Spatial Variability of Available Nitrogen in Different Sampling Scale——A Case Study on Cropland Soil in Yushu City[J].Research of Soil and Water Conservation,2012,19(01):106.
[4]孔萌,刘俊民.BP神经网络L-M优化算法在地下水动态预测中的应用[J].水土保持研究,2007,14(05):51.
　KONG Meng,LIU Jun-min.Applying the BP Net work L-M Optimize Arithmetic to Simulate and Forecast the Ground Water Dynamic[J].Research of Soil and Water Conservation,2007,14(01):51.
[5]石豫川,吉锋,冯文凯.BP神经网络在隧道岩爆预测中的应用[J].水土保持研究,2007,14(05):242.
　SHI Yu-chuan,JI Feng,FENG Wen-kai.Application of BP Neural Network in the Tunnel Rock Blasting[J].Research of Soil and Water Conservation,2007,14(01):242.
[6]王增彬,迟恒智.基于BP神经网络的济南市建设用地规模预测[J].水土保持研究,2007,14(05):245.
　WANG Zeng-bin,CHI Heng-zhi.Prediction of Urban Building Land in Jinan Based on ANN Model[J].Research of Soil and Water Conservation,2007,14(01):245.
[7]王艳,宋振柏,吴佩林.基于BP网络的高新技术产业用地适宜性评价[J].水土保持研究,2008,15(04):248.
　WANG Yan,SONG Zhen-bai,WU Pei-lin.Land Suitability Evaluation of High and New Technology Industry Based on BP Network[J].Research of Soil and Water Conservation,2008,15(01):248.
[8]刘登峰,齐实,韩小杰,等.缙云山不同土地利用类型地表径流水质评价[J].水土保持研究,2009,16(01):126.
　LIU Deng-feng,QI Shi,HAN Xiao-jie,et al.Surface Runoff Quality Evaluation of Several Land Uses in Jinyun Moutain[J].Research of Soil and Water Conservation,2009,16(01):126.
[9]蔡明科,胡国杰,许义和.基于人工神经网络的宝鸡市市区地下水调控方案研究[J].水土保持研究,2010,17(05):176.
　CAI Ming-ke,HU Guo-jie,XU Yi-he.Study on Regulation Schemes in the Urban Area of Baoji City with Artificial Neural Network[J].Research of Soil and Water Conservation,2010,17(01):176.
[10]武开福.基于灰色关联度与BP神经网络模型的日参考作物腾发量预测[J].水土保持研究,2011,18(02):237.
　WU Kai-fu.Discuss on Daily Crop Evapotranspiration Forecast Based on Gray Relation Analysis and BP Neural Network[J].Research of Soil and Water Conservation,2011,18(01):237.

备注/Memo

收稿日期:2013-10-26;改回日期:2013-12-06。
基金项目:国家水体污染控制与治理科技专项（2012ZX07202-009）
作者简介:卞建民(1968-),女,吉林延吉人,教授,博士生导师,主要从事地下水资源评价、水环境与水生态方面的研究。E-mail:bianjianmin@126.com

更新日期/Last Update: 1900-01-01