DOI: 10.1016/j.watres.2018.04.016
Scopus记录号: 2-s2.0-85047385883
论文题名: Integrating river hydromorphology and water quality into ecological status modelling by artificial neural networks
作者: Gebler D. ; Wiegleb G. ; Szoszkiewicz K.
刊名: Water Research
ISSN: 431354
出版年: 2018
卷: 139 起始页码: 395
结束页码: 405
语种: 英语
英文关键词: Habitat quality
; Hydromorphological status
; Macrophytes
; MultiLayer perceptron
; Multiple stressors
; Water framework directive
Scopus关键词: Environmental regulations
; Mean square error
; Multilayer neural networks
; Neural networks
; Rivers
; Sensitivity analysis
; Water conservation
; Water quality
; Habitat quality
; Hydromorphological status
; Macrophytes
; Multiple stressors
; Water Framework Directives
; River pollution
; nitrogen
; phosphorus
; artificial neural network
; computer simulation
; degradation
; fluvial geomorphology
; habitat quality
; institutional framework
; macrophyte
; physicochemical property
; prediction
; river water
; species diversity
; species richness
; water pollution
; water quality
; alkalinity
; Article
; artificial neural network
; conductance
; ecology
; macrophyte
; morphology
; physical chemistry
; priority journal
; river
; sensitivity analysis
; species richness
; water quality
; aquatic species
; ecosystem
; environmental monitoring
; plant
; theoretical model
; water pollution
; Aquatic Organisms
; Ecosystem
; Environmental Monitoring
; Models, Theoretical
; Neural Networks (Computer)
; Plants
; Rivers
; Water Pollution
; Water Quality
英文摘要: The aim of the study was to develop predictive models of the ecological status of rivers by using artificial neural networks. The relationships between five macrophyte indices and the combined impact of water pollution as well as hydromorphological degradation were examined. The dataset consisted of hydromorphologically modified rivers representing a wide water quality gradient. Three ecological status indices, namely the Macrophyte Index for Rivers (MIR), the Macrophyte Biological Index for Rivers (IBMR) and the River Macrophyte Nutrient Index (RMNI), were tested. Moreover two diversity indices, species richness (N) and the Simpson index (D) were tested. Physico-chemical parameters reflecting both water quality and hydromorphological status were utilized as explanatory variables for the artificial neural networks. The best modelling quality in terms of high values of coefficients of determination and low values of the normalized root mean square error was obtained for the RMNI and the MIR networks. The networks constructed for IBMR, species richness and the Simpson index showed a lower degree of fit. In all cases, modelling quality improved by adding two hydromorphological indices to the pool of explanatory variables. The significant effect of these indices in the models was confirmed by sensitivity analysis. The research showed that ecological assessment of rivers based on macrophyte metrics does not only reflect the water quality but also the hydromorphological status. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/112757
Appears in Collections: 气候减缓与适应
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作者单位: Department of Ecology and Environmental Protection, Poznan University of Life Sciences, Wojska Polskiego 28, Poznan, 60-637, Poland; Department of Ecology, Brandenburg University of Technology (BTU Cottbus-Senftenberg), Platz der Deutschen Einheit 1, Cottbus, 03046, Germany
Recommended Citation:
Gebler D.,Wiegleb G.,Szoszkiewicz K.. Integrating river hydromorphology and water quality into ecological status modelling by artificial neural networks[J]. Water Research,2018-01-01,139