文章摘要
白 云, 李 勇, 张万娟, 贺 嘉.基于粗糙集-贝叶斯网络的流域水质评价方法Journal of Water Resources and Water Engineering[J].,2022,33(5):1-10
基于粗糙集-贝叶斯网络的流域水质评价方法
Watershed water quality assessment method based on rough set and Bayesian network
  
DOI:10.11705/j.issn.1672-643X.2022.05.01
中文关键词: 水质评价  粗糙集理论  贝叶斯网络  属性约简  不确定性
英文关键词: water quality assessment  rough set (RS)  Bayesian network (BN)  attribute reduction  uncertainty
基金项目:国家自然科学基金项目(71801044); 国家社会科学基金项目(18CJY005)
Author NameAffiliation
BAI Yun1, LI Yong2, ZHANG Wanjuan3, HE Jia4 (1.重庆工商大学 管理科学与工程学院 重庆 400067 2.贵州医科大学 环境污染与疾病监控教育部重点实验室 贵州 贵阳 550025 3.西北农林科技大学 经济管理学院 陕西 杨凌 7121004.重庆工商大学 长江上游经济研究中心 重庆 400067) 
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中文摘要:
      针对现实流域水质评价中存在不完整和不确定信息这一问题,基于智能互补思想,提出了粗糙集-贝叶斯网络的流域水质评价方法。使用粗糙集理论提取影响流域水质状态的主要因子,得到最小属性约简集以降低建模复杂度;然后构造贝叶斯网络并进行模型训练,获得其网络结构和条件概率表,实现流域水质的概率决策推理;最后对嘉陵江流域重庆段的3个水质监测断面进行实例分析,验证该方法的有效性和准确性。结果表明:该方法可以正确进行流域水质评价推理,相比于其他方法(贝叶斯网络、灰色-贝叶斯网络、粗糙集-朴素贝叶斯),其具有最高的准确率(>0.97)、精确率(>0.86)、召回率(>0.86)和F1值(>0.86),为流域水环境管理提供了有效的技术支持。
英文摘要:
      According to the intelligent complementary strategy, a new watershed water quality assessment method based on rough set (RS) and Bayesian network (BN) was presented for the water quality assessment containing incomplete and uncertain information. Firstly, RS was used to extract the main factors affecting watershed water quality, so as to obtain the minimum attribute reduction set, which can be used to reduce the modelling complexity. Then, the BN was constructed and trained based on the attribute reduction set, and its network structure and conditional probability table were obtained to realize the probabilistic decision reasoning of watershed water quality. Finally, the model evaluation indexes were used to analyse three water quality monitoring sections in the Chongqing section of the Jialing River to verify the correctness and effectiveness of this method. The results show that this method is applicable to the watershed water quality assessment, and has the highest accuracy (>0.97), precision (>0.86), recall (>0.86), and F1-measure (>0.86) compared with other methods (BN, GRA-BN, RS- NB). This method can provide an effective technical support for the water environment management in the watershed.
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