DOI: 10.1016/j.watres.2017.12.010
Scopus记录号: 2-s2.0-85038026385
论文题名: Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic algorithm
作者: Sotomayor G. ; Hampel H. ; Vázquez R.F.
刊名: Water Research
ISSN: 431354
出版年: 2018
卷: 130 起始页码: 353
结束页码: 362
语种: 英语
英文关键词: Genetic algorithm
; Land cover
; Pattern recognition
; Water quality
Scopus关键词: Alkalinity
; Biochemical oxygen demand
; Chlorine compounds
; Dissolved oxygen
; Genetic algorithms
; Land use
; Nearest neighbor search
; Parameter estimation
; Pattern recognition
; River pollution
; Water quality
; Classification models
; Land cover
; Micro-biological parameters
; Multi-dimensional space
; Parameter optimisation
; Pattern recognition algorithms
; Pattern recognition method
; Water quality assessments
; Data mining
; dissolved oxygen
; nitric acid derivative
; oxygen
; data mining
; genetic algorithm
; land cover
; microbiology
; numerical method
; optimization
; parameter estimation
; pattern recognition
; physicochemical property
; spatial distribution
; water quality
; alkalinity
; Article
; biochemical oxygen demand
; catchment
; Ecuador
; electric conductivity
; fecal coliform
; genetic algorithm
; land use
; pattern recognition
; priority journal
; river basin
; turbidity
; water hardness
; water quality
; water sampling
; water supply
; algorithm
; analysis
; chemistry
; cluster analysis
; data mining
; environmental monitoring
; feces
; procedures
; river
; standards
; water pollutant
; Ecuador
; Paute Basin
; Algorithms
; Biological Oxygen Demand Analysis
; Cluster Analysis
; Data Mining
; Ecuador
; Electric Conductivity
; Environmental Monitoring
; Feces
; Nitrates
; Oxygen
; Rivers
; Water Pollutants, Chemical
; Water Quality
英文摘要: A non-supervised (k-means) and a supervised (k-Nearest Neighbour in combination with genetic algorithm optimisation, k-NN/GA) pattern recognition algorithms were applied for evaluating and interpreting a large complex matrix of water quality (WQ) data collected during five years (2008, 2010–2013) in the Paute river basin (southern Ecuador). 21 physical, chemical and microbiological parameters collected at 80 different WQ sampling stations were examined. At first, the k-means algorithm was carried out to identify classes of sampling stations regarding their associated WQ status by considering three internal validation indexes, i.e., Silhouette coefficient, Davies-Bouldin and Caliński-Harabasz. As a result, two WQ classes were identified, representing low (C1) and high (C2) pollution. The k-NN/GA algorithm was applied on the available data to construct a classification model with the two WQ classes, previously defined by the k-means algorithm, as the dependent variables and the 21 physical, chemical and microbiological parameters being the independent ones. This algorithm led to a significant reduction of the multidimensional space of independent variables to only nine, which are likely to explain most of the structure of the two identified WQ classes. These parameters are, namely, electric conductivity, faecal coliforms, dissolved oxygen, chlorides, total hardness, nitrate, total alkalinity, biochemical oxygen demand and turbidity. Further, the land use cover of the study basin revealed a very good agreement with the WQ spatial distribution suggested by the k-means algorithm, confirming the credibility of the main results of the used WQ data mining approach. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/113057
Appears in Collections: 气候减缓与适应
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作者单位: Laboratorio de Ecología Acuática, Departamento de Recursos Hídricos y Ciencias Ambientales, Universidad de Cuenca, Av. 12 de Abril S/N, Cuenca, Ecuador; Facultad de Ciencias Químicas, Universidad de Cuenca, Av. 12 de Abril S/N, Cuenca, Ecuador; Facultad de Ingeniería, Universidad de Cuenca, Av. 12 de Abril S/N, Cuenca, Ecuador
Recommended Citation:
Sotomayor G.,Hampel H.,Vázquez R.F.. Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic algorithm[J]. Water Research,2018-01-01,130