基于智能算法的土壤环境质量PPC评价模型的比较研究
投稿时间:2013-04-23  修订日期:2013-08-07  点此下载全文
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何厅厅 中国矿业大学北京土地复垦与生态重建研究所 tthe2011@163.com 
赵艳玲 中国矿业大学(北京)  
侯占东 中国矿业大学(北京)  
曾继勇 中国矿业大学(北京)  
李源 中国矿业大学(北京)  
王亚云 中国矿业大学(北京)  
刘亚萍 中国矿业大学(北京)  
中文摘要:土壤环境质量的数字特征复杂,其污染评价属于高维非线性数据聚类问题,可以用PPC模型进行土壤环境质量分类。在土壤环境质量PPC评价模型的应用中,最佳投影方向直接影响评价结果的精度。为获得更好的投影方向,本文将细菌算法(BFA)、遗传算法(GA)、鱼群算法(FSA)算得的投影方向分别用于PPC模型中,构建了土壤环境质量BFA-PPC、GA-PPC、FSA-PPC评价模型。对某地区农业土壤环境质量分别应用3种评价模型分别进行综合评价,比较3种智能算法确定的投影方向及其对应的评价结果,表明:3种评价模型在土壤环境质量评价中适用;3种智能算法算得的投影方向均合理,且FSA最准确地反映了各评价指标在评价过程中的重要程度,其次是GA、BFA。
中文关键词:关 键 词:土壤环境质量  细菌算法  遗传算法  鱼群算法
 
Comparative study on the soil environmental quality evaluation of PPC model based on Intelligent Algorithms
Abstract:The soil heavy metals’ digital characteristics have a complex feature, and its pollution evaluation was a clustering problem of high dimensional nonlinear data, therefore, the classification of soil heavy metal pollution can be used by PPC model. In the application of PPC evaluation model for soil heavy metals, the best projection direction can directly affect the accuracy of the evaluation results. To obtain a better projection direction, this paper modular calculates the projection direction by bacterial foraging algorithm (BFA)、genetic algorithm (GA)、fish swarm algorithm (FSA) which were applied for the PPC model respectively, and the soil heavy metal pollution evaluation was constructed by using BEA-PPC、GA-PPC、FSA-PPC model, respectively. The aforementioned 3 models were then applied in a region, to evaluate the agricultural soil heavy metal, the projection direction and the corresponding evaluation results were compared , which show that, the 3 evaluation models are applicable in the evaluation of the soil heavy metal pollution; and the projection direction of these three intelligent algorithm are reasonable, moreover the FSA accurately reflects the heavy metal in the evaluation process of the most important degree, followed by GA, BFA..
keywords:Soil heavy metal pollution  Bacterial foraging algorithm  Genetic algorithm  Fish swarm algorithm.
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