数据驱动的模糊支持向量农业水质评价模型
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摘要
[目的]针对在线农业水质综合评价中的监测数据噪声及边界模糊问题,建立具有良好抗扰性和等级划分的综合评价模型。[方法]提出了基于数据确定投影寻踪指标权重及模糊隶属度参数的支持向量评价模型。采用改进遗传算法对投影寻踪函数进行了优化求解,获得相对客观的指标权重向量,而后结合数据优化模糊隶属度参数,构建模糊支持向量综合评价模型,以使得监测噪声对评价模型泛化能力的影响减小。此外,考虑到通用的离散化评价等级分辨率较低,提出了区域划分信度的概念,用以辅助说明样本所属区域划分等级的可信程度,实现对综合评价结果进行细化补充说明的目的。[结果]评价模型与专家意见及传统评价方法的结果吻合程度较高,且在监测数据叠加10%至30%的随机噪声时,模型仍能保持85%以上的一致率,样本的区域划分可信度均大于临界值,抗扰效果优于传统模糊综合评价及灰色聚类法。[结论]本文构建的模型具有较好的可行性与鲁棒性,能为后续噪声存在条件下农业水质在线实时综合评价提供借鉴与参考。
[Objective]We aimed to solve the problem of monitoring data noise and boundary ambiguity in comprehensive evaluation of agricultural water quality,in order to establish a comprehensive evaluation model with good disturbance resistance and grade division.[Methods]A data-driven fuzzy support vector evaluation method was proposed to determine index weight of projection pursuit index and the parameters of fuzzy membership.Improved genetic algorithm was adapted to optimize the projection pursuit function and obtain the relatively objective index weigh.Then the parameters of fuzzy membership were optimized with data,and a comprehensive evaluation model of fuzzy support vector machine was constructed to reduce the influence of monitoring noise on the generalization ability of the evaluation model.In addition,considering the low resolution of the general discrete evaluation grade,the concept of regional division reliability was proposed to explain the reliability of the regional division grade of the sample,to further explain the comprehensive evaluation results.[Results]The model evaluation results were consistent with the results from experts and traditional evaluation.The model maintained more than 85% consistent rate with the monitoring data with 10%～30% random noise,and the reliability of regional division of samples was greater than the critical value,indicating the reliability and robustness of the method.The results from the constructed model were better than the fuzzy comprehensive evaluation and grey clustering method.[Conclusion]The method proposed by the present study is feasible and robust,and it can provide a reference for real-time evaluation of agricultural water quality under the condition of subsequent noise.
[Objective]We aimed to solve the problem of monitoring data noise and boundary ambiguity in comprehensive evaluation of agricultural water quality,in order to establish a comprehensive evaluation model with good disturbance resistance and grade division.[Methods]A data-driven fuzzy support vector evaluation method was proposed to determine index weight of projection pursuit index and the parameters of fuzzy membership.Improved genetic algorithm was adapted to optimize the projection pursuit function and obtain the relatively objective index weigh.Then the parameters of fuzzy membership were optimized with data,and a comprehensive evaluation model of fuzzy support vector machine was constructed to reduce the influence of monitoring noise on the generalization ability of the evaluation model.In addition,considering the low resolution of the general discrete evaluation grade,the concept of regional division reliability was proposed to explain the reliability of the regional division grade of the sample,to further explain the comprehensive evaluation results.[Results]The model evaluation results were consistent with the results from experts and traditional evaluation.The model maintained more than 85% consistent rate with the monitoring data with 10%～30% random noise,and the reliability of regional division of samples was greater than the critical value,indicating the reliability and robustness of the method.The results from the constructed model were better than the fuzzy comprehensive evaluation and grey clustering method.[Conclusion]The method proposed by the present study is feasible and robust,and it can provide a reference for real-time evaluation of agricultural water quality under the condition of subsequent noise.
引文
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[9]梁中耀,张雨宇,钱松,等.基于二项分布检验法的水质达标评价方法研究[J].环境科学学报,2017,37(1):339-346.
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[12]Feng Kai,Lu Jiangang,Chen Jinshui.Nonlinear model predictive control based on support vector machine and genetic algorithm[J].Chinese Journal of Chemical Engineering,2015,23(12):2048-2052.
[13]李婷,王波.DNA中一类非线性动力学方程的数值解[J].应用数学进展,2015,4(3):277-284.
[14]Vapnik V N.The nature of statistical learning theory[M].Germany,Berlin:Springer-Verlag,1995.
[15]张峰,薛惠锋,WANG Wei,等.一种模态-支持向量机水资源监测异常数据重构方法[J].农业机械学报,2017(11):1-13.
[16]Lin Chunfu,Wang Shengde.Fuzzy support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):464-471.
[17]Chiang J H,Hao Peiyi.A new kernel-based fuzzy clustering approach:Support vector clustering with cell growing[J].IEEE Transactions on Fuzzy Systems,2003,11(4):518-527.
[18]Yi Lin.Support vector machines and the bayes rule in classification[J].Data mining and Knowledge Discovery,2002,6(3):259-275.
[19]Huang Hanping,Liu Yihung.Fuzzy support vector machines for pattern recognition and data mining[J].International Journal of Fuzzy Systems,2002,4(3):826-835.
[20]许翠云,业宁.基于类向心度的模糊支持向量机[J].计算机工程与科学,2014,36(8):1623-1628.
[21]鞠哲,曹隽喆,顾宏.用于不平衡数据分类的模糊支持向量机算法[J].大连理工大学学报,2016,56(5):525-531.
[22]李飞,黄瑾辉,李雪,等.基于随机模糊理论的土壤重金属潜在生态风险评价及溯源分析[J].环境科学学报,2015,35(4):1233-1240.
[23]张礼兵,程吉林,金菊良,等.农业灌溉水质评价的投影寻踪模型[J].农业工程学报,2006,22(4):15-18.
[2]高学平,孙博闻,訾天亮,等.基于时域权重矩阵的模糊综合水质评价法及其应用[J].环境工程学报,2017,11(2):970-976.
[3]姜秋香,付强,王子龙.基于粒子群优化投影寻踪模型的区域土地资源承载力综合评价[J].农业工程学报,2011,27(11):319-324.
[4]金菊良,吴开亚,郦建强.巢湖水质安全评价的对应分析和投影寻踪熵耦合方法[J].四川大学学报:工程科学版,2007,39(6):7-13.
[5]于倩雯,吴凤平.面板数据下水资源安全的灰色聚类评估[J].科技管理研究,2016,36(19):64-69.
[6]刘东,龚方华,付强,等.基于博弈论赋权的灌溉用水效率GRA-TOPSIS评价模型[J].农业机械学报,2017,48(5):218-226.
[7]余勋,梁婕,曾光明,等.基于三角模糊数的贝叶斯水质评价模型[J].环境科学学报,2013,33(3):904-909.
[8]巩奕成,张永祥,丁飞,等.基于萤火虫算法的投影寻踪地下水水质评价方法[J].中国矿业大学学报,2015,44(3):566-572.
[9]梁中耀,张雨宇,钱松,等.基于二项分布检验法的水质达标评价方法研究[J].环境科学学报,2017,37(1):339-346.
[10]陈曜,丁晶,赵永红.基于投影寻踪原理的四川省洪灾评估[J].水利学报,2010,41(2):220-225.
[11]Lan Young,Gropp K,Fazil A,et al.Knowledge synthesis to support risk assessment of climate change impacts on food and water safety:A case study of the effects of water temperature and salinity on Vibrio parahaemolyticus,in raw oysters and harvest waters[J].Food Research International,2015,68:86-93.
[12]Feng Kai,Lu Jiangang,Chen Jinshui.Nonlinear model predictive control based on support vector machine and genetic algorithm[J].Chinese Journal of Chemical Engineering,2015,23(12):2048-2052.
[13]李婷,王波.DNA中一类非线性动力学方程的数值解[J].应用数学进展,2015,4(3):277-284.
[14]Vapnik V N.The nature of statistical learning theory[M].Germany,Berlin:Springer-Verlag,1995.
[15]张峰,薛惠锋,WANG Wei,等.一种模态-支持向量机水资源监测异常数据重构方法[J].农业机械学报,2017(11):1-13.
[16]Lin Chunfu,Wang Shengde.Fuzzy support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):464-471.
[17]Chiang J H,Hao Peiyi.A new kernel-based fuzzy clustering approach:Support vector clustering with cell growing[J].IEEE Transactions on Fuzzy Systems,2003,11(4):518-527.
[18]Yi Lin.Support vector machines and the bayes rule in classification[J].Data mining and Knowledge Discovery,2002,6(3):259-275.
[19]Huang Hanping,Liu Yihung.Fuzzy support vector machines for pattern recognition and data mining[J].International Journal of Fuzzy Systems,2002,4(3):826-835.
[20]许翠云,业宁.基于类向心度的模糊支持向量机[J].计算机工程与科学,2014,36(8):1623-1628.
[21]鞠哲,曹隽喆,顾宏.用于不平衡数据分类的模糊支持向量机算法[J].大连理工大学学报,2016,56(5):525-531.
[22]李飞,黄瑾辉,李雪,等.基于随机模糊理论的土壤重金属潜在生态风险评价及溯源分析[J].环境科学学报,2015,35(4):1233-1240.
[23]张礼兵,程吉林,金菊良,等.农业灌溉水质评价的投影寻踪模型[J].农业工程学报,2006,22(4):15-18.
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