长江科学院院报 ›› 2016, Vol. 33 ›› Issue (10): 18-23.DOI: 10.11988/ckyyb.20150696

• 水资源与环境 • 上一篇    下一篇

基于SSO-PP模型的滇池流域水质综合评价

吴光琼,方金鑫   

  1. 云南省水文水资源局 丽江分局,云南 丽江 674100
  • 收稿日期:2015-08-22 出版日期:2016-10-20 发布日期:2016-10-17
  • 作者简介:吴光琼(1971-),女,云南丽江人,工程师,主要从事水文与水资源等研究,(电话)13987049121(电子信箱)1258525280@qq.com。

Comprehensive Evaluation of Water Quality in Dianchi Watershed Based on SSO-PP model

WU Guang-qiong, FANG Jin-xin   

  1. Lijiang Branch of Yunnan Provincial Hydrology Water Resources Bureau, Lijiang 674100, China
  • Received:2015-08-22 Published:2016-10-20 Online:2016-10-17

摘要: 针对投影寻踪(PP)模型在实际应用中最佳投影方向a难以确定的不足,利用一种新型群体智能仿生算法——群居蜘蛛优化算法(SSO)搜寻PP模型最佳投影方向a,提出SSO-PP水质综合评价模型。以云南省滇池流域4个监测断面2003—2013年水质评价为例进行实例研究,选取对水体影响较大的氨氮等5项水质评价因子,利用各指标标准阈值z构造水质综合评价分级标准。结果表明①SSO-PP模型水质评价结果与单因子法评价结果基本相同,但对于断面1#,2#,4#,部分年度水质的评价结果要优于单因子法评价结果1~2级。通过Kendall统计量检验,断面3#—4#水质改善趋势相对明显。②SSO-PP模型评价结果客观、合理,能够有效应用于水质综合评价。

关键词: 水质评价, 投影寻踪, 群居蜘蛛优化算法, 群体智能, 滇池流域

Abstract: In order to overcome the difficulty of determining the optimal projection direction in practical application of projection pursuit (PP) model, we propose a method of searching the optimal projection direction by using Social Spiders Optimization (SSO) algorithm, and hence building a SSO-PP model of water quality assessment. Four monitoring sections of Dianchi Lake watershed in Yunnan province from 2003 to 2013 were taken as case study. Fiver factors which have big influences on water quality were selected as assessment indicators: NH3-N, TN, CODMn, BOD5, and TP. The rating criteria of water quality were obtained according to index standard thresholds. Results showed that the assessment results of SSO-PP model were consistent with those of single-factor analysis, some superior for section 1#, 2#, and 4#. Kendall statistical test showed that the water quality of section 3# and section 4# improved apparently. The results of SSO-PP model are objective and reasonable, indicating that the model can be applied to assessing water quality effectively.

Key words: water quality assessment, projection pursuit, social spider optimization algorithm, swarm intelligence, Dianchi watershed

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