Fuzzy modelling to identify key drivers of ecological water quality to support decision and policy making. Issue 68 (February 2017)

• Record Type: Journal Article
• Authors: Forio, Marie Anne Eurie; Mouton, Ans; Lock, Koen; Boets, Pieter; Nguyen, Thi Hanh Tien; Damanik Ambarita, Minar Naomi; Musonge, Peace Liz Sasha; Dominguez-Granda, Luis; Goethals, Peter L.M.
• Is Part Of: Environmental science & policy. Issue 68(2017:Feb.)
• Page Start: 58
• Page End: 68
• Publication Date: 2017-02
• Subjects: Fuzzy logic -- Environmental variables -- Decision support systems -- River basin management; Environmental policy -- Periodicals; Environmental sciences -- Periodicals; Environnement -- Politique gouvernementale -- Périodiques; Sciences de l'environnement -- Périodiques; Environmental policy; Environmental sciences; Periodicals; Electronic journals; 363.70561
• DOI: 10.1016/j.envsci.2016.12.004
• Languages: English
• ISSNs: 1462-9011

Abstract

Highlights: Fuzzy models were developed for analysing ecological water quality (EWQ). Determination of relevant input variables and use of expert knowledge are key strengths. Land use in the Guayas basin had a dominant effect on the EWQ expressed as BMWP/Col. Multivariate effects were considered via sensitivity analyses. Fuzzy logic models can support spatial planning and water policy development. Abstract: Water quality modelling is an effective tool to investigate, describe and predict the ecological state of an aquatic ecosystem. Various environmental variables may simultaneously affect water quality. Appropriate selection of a limited number of key-variables facilitates cost-effective management of water resources. This paper aims to determine (and analyse the effect of) the major environmental variables predicting ecological water quality through the application of fuzzy models. In this study, a fuzzy logic methodology, previously applied to predict species distributions, was extended to model environmental effects on a whole community. In a second step, the developed models were applied in a more general water management context to support decision and policy making. A hill-climbing optimisation algorithm was applied to relate ecological water quality and environmental variables to the community indicator. The optimal model was selected based on the predictive performance (Cohen's Kappa), ecological relevance and model's interpretability. Moreover, a sensitivity analysis was performed as an extra element to analyse and evaluate the optimal model. The optimal model included the variables land use, chlorophyll and flow velocity. The variable selection method and sensitivity analysis indicated that land use influences ecological water quality the most and that it affects the effect of other variables on water quality to a high extent. The model outcome can support spatial planning related to land use in river basins and policy making related to flows and water quality standards. Fuzzy models are transparent to a wide range of users and therefore may stimulate communication between modellers, river managers, policy makers and stakeholders.