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Integrating data-driven ecological models in an expert-based decision support system for water management in the Du river basin (Vietnam)
Hoang, T.H.; Mouton, A.; Lock, K.; De Pauw, N.; Goethals, P.L.M. (2013). Integrating data-driven ecological models in an expert-based decision support system for water management in the Du river basin (Vietnam). Environ. Monit. Assess. 185(1): 631-642. https://dx.doi.org/10.1007/s10661-012-2580-6
In: Environmental Monitoring and Assessment. Kluwer: Dordrecht. ISSN 0167-6369; e-ISSN 1573-2959, more
Peer reviewed article  
Available in  Authors 
Keyword
    Fresh water
Author keywords
    Classification trees; Ecological modelling; Integrated river management;River restoration; European Water Framework Directive; Decision supportsystem
Authors  Top 
  • Hoang, T.H.
  • Mouton, A., more
  • Lock, K., more
Abstract
    In this study, classification trees were combined with the Water Framework Directive (WFD)-Explorer, a modular toolbox that supports integrated water management in a river basin to evaluate the impact of different restoration measures on river ecology. First, the WFD-Explorer toolbox analysed the effect of different restoration options on the abiotic river characteristics based on the water and substance balance embedded in the simulation environment. Based on these abiotic characteristics, the biological index Biological Monitoring Working Party for Vietnam was then predicted by classification trees that were trained on biological and abiotic data collected in the Du river basin in northern Vietnam. The ecological status of streams in the basin ranged from nearly pristine headwaters to severely impacted river stretches. Elimination of point sources from ore extraction and decentralised domestic wastewater treatment proved to be the most effective measures to improve the ecological condition of the Du river basin. The combination of the WFD-Explorer results with data-driven models enabled model application in a situation where expert knowledge was lacking. Consequently, this approach appeared promising for decision support in the context of river restoration and conservation management.
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