赣江南昌段水源地水质预报模型研究-环境工程专业论文.docxVIP

摘 要 本研究通过对地表水水质污染物资料的分析，发现水质污染物浓度的变化具 有较强的非线性特性，通过对不同预报方法进行比较后，选择了能捕捉非线性变 化规律的、基于改进的BP人工神经网络预报方法，与常规方法不同，该方法以 实验数据为样本，利用网络的自学习和联想记忆能力对实验数据进于亍学习，具有 极佳的函数逼近能力，能很好的拟合历史样本，达到识别各影响因子之问复杂的 非线性映射关系，为提高水质预测模型的精度和环境决策部门制定中长期水环境 保护和治理规划提供了有效的手段。 在此基础上，建立了一整套适合赣江南昌段水源地生米监测断面商锰酸盐指 数和氨氮浓度的预报模型，使得高锰酸盐指数预报精度除12月份低于70％，其 余月份预报精度均在70％以上，氨氮浓度的预报精度除8月份低于70％，其余 月份预报精度均在70％以上，预报精度较高，效果较好。 本研究采用VC语言编写开发了水质预报系统，该系统适用于Windows平 台，界面友好，操作方便，数据的输入简单明了，输出结果直观，能有效地为决 策分析服务。 关键词：非线性；BP人工神经网络；预报模型；水质预报  Abstract By the analysis of the monitoring data about the pollutant in surface water,this research discovered that the movement of the pollutant concentration in surface water has a rather strong non—linear characteristic．After comparing different forecast methods，We selected the BP Artificial Neural Network forecast method，which is based on the ameliorations and Can catch the non-linear movement law completely． This method iS different from other routine methods．It uses experiment data as the swatch,and studies on them by the use of t11e network’S self-study and association memory abilities．It has a quite good function approach ability,SO it Can accord with the historic swatches commendabl％and Can achieve the aim of identifying the complicated non·linear mapping relationship among each influence factor．Besides，it call improve the precision ofthe water quality predict model．Furthermore，it provides an effective means for the environmental decision-making department to program the water environmental protection and treatment． On this basis，this research has set up a series of forecast model，which suits the permanganate exponent and ammonia nitrogen concentration of Shengmi monitoring section of water resource of Ganjiang Nanchang area．As a result，the forecast precision of permanganate exponent Can achieve upwards of 70％on every month of the year,except for December,on which the precision is below 70％．So does the ammonia nitrogen concentration on every month of the year,except for August，on which t