Bayesian belief network models to analyse and predict ecological water quality in rivers
Forio, M.A.E.; Landuyt, D.; Bennetsen, E.; Lock, K.; Nguyen, T.H.T.; Damanik-Ambarita, M.N.; Musonge, P.L.S.; Boets, P.; Everaert, G.; Dominguez-Granda, L.; Goethals, P.L.M. (2015). Bayesian belief network models to analyse and predict ecological water quality in rivers. Ecol. Model. 312: 222-238. https://dx.doi.org/10.1016/j.ecolmodel.2015.05.025
In: Ecological Modelling. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0304-3800; e-ISSN 1872-7026, more
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| Author keywords |
Macroinvertebrates; Restoration simulations; River basin management;Decision support systems; Bayesian belief networks |
| Authors | | Top |
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- Nguyen, T.H.T., more
- Damanik-Ambarita, M.N., more
- Musonge, P.L.S., more
- Boets, P., more
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- Everaert, G., more
- Dominguez-Granda, L.
- Goethals, P.L.M., more
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| Abstract |
Economic growth is often based on the intensification of crop production, energy consumption and urbanization. In many cases, this leads to the degradation of aquatic ecosystems. Modelling water resources and the related identification of key drivers of change are essential to improve and protect water quality in river basins. This study evaluates the potential of Bayesian belief network models to predict the ecological water quality in a typical multifunctional and tropical river basin. Field data, expert knowledge and literature data were used to develop a set of Bayesian belief network models. The developed models were evaluated based on weighted Cohen's Kappa (κw), percentage of correctly classified instances (CCI) and spherical payoff. On top, a sensitivity analysis and practical simulation tests of the two most reliable models were performed. Cross-validation based on κw (Model 1: 0.44 ± 0.08; Model 2: 0.44 ± 0.11) and CCI (Model 1: 36.3 ± 2.3; Model 2: 41.6 ± 2.3) indicated that the performance was reliable and stable. Model 1 comprised of input variables main land use, elevation, sediment type, chlorophyll, flow velocity, dissolved oxygen, and chemical oxygen demand; whereas Model 2 did not include dissolved oxygen and chemical oxygen demand. Although the predictive performance of Model 2 was slightly higher than that of Model 1, simulation outcomes of Model 1 were more coherent. Additionally, more management options could be evaluated with Model 1. As the model's ability to simulate management outcomes is of utmost importance in model selection, Model 1 is recommended as a tool to support decision-making in river management. Model predictions and sensitivity analysis indicated that flow velocity is the major variable determining ecological water quality and suggested that construction of additional dams and water abstraction within the basin would have an adverse effect on water quality. Although a case study in a single river basin is presented, the modelling approach can be of general use on any other river basin. |
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