• (1)2008-2021年连续14年获得中国科技核心期刊
  • (2)2021年被中国科学院文献情报中心中国科学引文数据库CSCD(核心库)收录(2021-2022)
  • (3)热烈庆祝《水资源与水工程学报》入编北京大学图书馆《中文核心期刊要目总览》2020年版
程麒铭, 陈 垚, 刘 臻, 唐颖辉, 袁绍春.基于随机森林-投影寻踪法的生物滞留系统多目标评价方法水资源与水工程学报[J].,2022,33(4):85-90
基于随机森林-投影寻踪法的生物滞留系统多目标评价方法
Multi-objective evaluation method of bioretention system based on random forest-projection pursuit method
  
DOI:10.11705/j.issn.1672-643X.2022.04.12
中文关键词:  生物滞留系统  投影寻踪法  随机森林  多目标评价  遗传算法  粒子群算法
英文关键词:bioretention system  projection pursuit (PP)  random forest (RF)  multi-objective evaluation  genetic algorithm (GA)  particle swarm optimization (PSO)
基金项目:重庆市研究生联合培养基地项目(JDLHPYJD2019);国家自然科学基金项目(51709024);重庆市建设科技计划项目(城科字2020第5-7);重庆市自然科学基金项目(cstc2020jcyj-msxmX0716、cstc2020jcyj-msxmX1000)
作者单位
程麒铭1, 陈 垚1,2, 刘 臻1,2, 唐颖辉1, 袁绍春1,2 (1.重庆交通大学 河海学院 重庆 400074 2.重庆交通大学 环境水利工程重庆市工程实验室 重庆 400074) 
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中文摘要:
      生物滞留系统性能受植物和介质土的影响显著,且各性能指标具有多样性和不相容性,而传统评价方法具有较强的主观性易导致评价结果出错。采用随机森林模型对原始数据进行特征筛选以降低数据维度,构建投影寻踪模型对不同植物和土壤的水力渗透性能和污染物去除性能进行多目标评价,并利用高鲁棒性的遗传算法(GA)和粒子群算法(PSO)进行模型求解。植物评价结果表明,风车草为生物滞留系统的最佳植物,且评价结果与层次分析模型和BP神经网络模型方法相似;介质土评价结果表明,RST2(9.8%壤砂土+88.2%细砂+2%蛭石)为生物滞留系统的最佳介质土配置方案,且评价结果与传统投影寻踪法相似。研究结果证实了随机森林-投影寻踪(RF-PP)模型适用于生物滞留系统多目标评价。
英文摘要:
      The performance of the bioretention system is significantly affected by plants and soil media, and its various diversified performance indexes are incompatible. However, the conventional evaluation methods are flawed by their strong subjectivity, which could easily lead to the errors of evaluation results. Here, the random forest (RF) model was used to screen the original data so as to reduce the dimensionality of the data. Then, the projection pursuit (PP) model was constructed to evaluate the hydraulic permeability and pollutant removal performance of different plants and soil media for multi-objective evaluation, and the model was solved by genetic algorithm (GA) and particle swarm optimization (PSO). The results of plant evaluation showed that Cyperus alternifolius L. was the optimal plant for the bioretention system, which was similar to the results of the analytic hierarchy process model and the back propagation (BP) neural network model. The evaluation results of soil media showed that RST2 (9.8% loamy sand+88.2% fine sand+2.0% vermiculite) was the optimal soil media configuration for the bioretention system, which was similar to the conclusion of the conventional projection pursuit method. These findings indicate that RF-PP model is suitable for multi-objective evaluation of the bioretention system.
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