遗传神经网络在水质监测点优化布局中的应用
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摘要
为科学全面地掌握湖泊水域的水质指标分布状况,以广西大学镜湖为实验研究对象,以无人船为平台将RBF神经网络与遗传算法相结合,对监测点的优化选择进行了讨论研究。分别对水质参数的单目标与多目标进行优化实验,并绘制拟合分布图。结果表明:遗传神经网络比传统的等距监测在水质参数的分布误差上具有明显优势,并在优化后至少6周内,其结果反映的数据依旧准确有效,说明遗传神经网络可明显提高水质监测的效率。
In order to grasp the distribution of water quality indexes in lake water scientifically and comprehensively,this paper took Jinghu Lake of Guangxi University as the experimental object,and unmanned ship as the platform,discussed the optimization selection of monitoring points based on combining RBF neural network with genetic algorithm. The single-objective or multi-objective optimization of water quality parameters was tested respectively,and fitting distribution map was formulated.The results showed that the genetic neural network had more obvious advantages in the distribution error of water quality parameters than the traditional isometric monitoring,and the data reflected by the results was still accurate and effective at least 6 weeks after optimization,indicating that genetic neural network could significantly improve the efficiency of water quality monitoring.
In order to grasp the distribution of water quality indexes in lake water scientifically and comprehensively,this paper took Jinghu Lake of Guangxi University as the experimental object,and unmanned ship as the platform,discussed the optimization selection of monitoring points based on combining RBF neural network with genetic algorithm. The single-objective or multi-objective optimization of water quality parameters was tested respectively,and fitting distribution map was formulated.The results showed that the genetic neural network had more obvious advantages in the distribution error of water quality parameters than the traditional isometric monitoring,and the data reflected by the results was still accurate and effective at least 6 weeks after optimization,indicating that genetic neural network could significantly improve the efficiency of water quality monitoring.
引文
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[2]赵琰鑫,赵翠平,陈岩.国内外流域水环境模型法规化建设现状和对比研究[J].环境污染与防治,2016,38(7):82-87.
[3]曹波,高怡宁.水质监测布点优化模型设计[J].科协论坛(下半月),2013(2):137-138.
[4]江立文,陈杨,林暾,等.瑶湖湖泊水质监测点的优化布设研究[J].环境科学与技术,2012,35(9):197-200.
[5]刘书明,李振瑜,陈晋端,等.基于覆盖水量的监测点优化选址法的缺陷与改进[J].环境科学,2010,31(1):88-92.
[6]刘书明,吴雪,欧阳乐岩.不确定节点水量下水质监测点优化选址方法[J].环境科学,2013,34(8):3108-3112.
[7]李龙云,李树平,张春杰,等.配水系统中水质监测点的优化布置[J].供水技术,2008,2(3):47-50.
[8]张月霞,毛金龙.层次聚类分析法在水质监测点优化中的应用[J].环境科学导刊,2016,35(1):20-25.
[9]崔海波,殷永泉,崔兆杰,等.动态贴近度法在徒骇河干流水质监测点优化中的应用[J].环境监控与预警,2015,7(3):17-21.
[10]郑连臣,刘志斌,周岩,等.基于对应分析法的地下水水质监测点优化[J].辽宁工程技术大学学报,2007,26(增刊2):260-262.
[11]甘宇,殷实,王辉,等.物元分析法的改进及在辽河干流水质监测断面优化中的应用[J].环境监测管理与技术,2017,29(3):8-12.
[12]陈飞香,程家昌,胡月明,等.基于RBF神经网络的土壤铬含量空间预测[J].地理科学,2013,33(1):69-74.
[13]冯惠妍,陈争光,蔡月芹.RBF神经网络的土壤养分肥力评价研究[J].黑龙江八一农垦大学学报,2015,27(4):99-102.
[14]梁伟臻.人工神经网络改进BP算法优化水质监测点[J].广州环境科学,2005(3):32-34,40.
[15]刘浩然,赵翠香,李轩,等.一种基于改进遗传算法的神经网络优化算法研究[J].仪器仪表学报,2016,37(7):1573-1580.
[16]宫玉琳,文大化,张静鹤.基于粒子群优化算法和RBF神经网络的水质评价方法研究[J].长春理工大学学报(自然科学版),2014,37(5):141-145.
[17]王旭,雷晓辉,蒋云钟,等.基于可行空间搜索遗传算法的水库调度图优化[J].水利学报,2013,44(1):26-34.
[18] Manojkumar Ramteke,Nitish Ghune,Vibhu Trivedi. Simulated binary jumping gene:a step towards enhancing the performance of real-coded genetic algorithm[J]. Information Sciences,2015,325:429-454.
[19]胡贵强.多目标优化的遗传算法及其实现[J].重庆文理学院学报(自然科学版),2008,27(5):12-15.
[20]郑海利,鞠勤国,刘美侠,等.基于多目标遗传算法的水资源优化配置[J].东北水利水电,2011,29(5):54-57,72.
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