A review of Bayesian model averaging approach for urban drainage water quality modeling

The uncertainty in urban drainage water quality modeling is highly relevance in any practical application. Several models are available in the literature for such tasks, and one of the most problematic choices is the selection of the most appropriate approach for the specific application. The Bayesian Model Averaging approach attempts to support the modeler in such choices by providing a method to identify and select the best performing models and average their output response to reduce the related uncertainty. In the current report, the Bayesian Model Averaging is proposed and discussed. The method is one of the most promising uncertainty-based model selection tool and it is able to improve the selection of the most appropriate model for a specific case once new knowledge is available. The method is theoretically discussed and conclusions on possible application are drawn.

[1]  S. Sorooshian,et al.  Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .

[2]  Gabriele Freni,et al.  Modelling of E. coli distribution in coastal areas subjected to combined sewer overflows. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[3]  C. Granger,et al.  Improved methods of combining forecasts , 1984 .

[4]  Gabriele Freni,et al.  Quantification of diffuse and concentrated pollutant loads at the watershed-scale: an Italian case study. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[5]  Giuseppe Ciraolo,et al.  Wind- and tide-induced currents in the Stagnone lagoon (Sicily) , 2012, Environmental Fluid Mechanics.

[6]  Hamid Moradkhani,et al.  Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation , 2012 .

[7]  Barbara Milici,et al.  Effects of roughness on particle dynamics in turbulent channel flows: a DNS analysis , 2014, Journal of Fluid Mechanics.

[8]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[9]  Luca Vezzaro,et al.  Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling. , 2012, Water research.

[10]  Gabriele Freni,et al.  Urban Storm-Water Quality Management: Centralized versus Source Control , 2010 .

[11]  Kuolin Hsu,et al.  A sequential Bayesian approach for hydrologic model selection and prediction , 2009 .

[12]  W. Rauch,et al.  Assessing uncertainties in urban drainage models , 2012 .

[13]  G. La Loggia,et al.  Uncertainty in urban flood damage assessment due to urban drainage modelling and depth-damage curve estimation. , 2010, Water science and technology : a journal of the International Association on Water Pollution Research.

[14]  Jasper A. Vrugt,et al.  Comparison of point forecast accuracy of model averaging methods in hydrologic applications , 2010 .

[15]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[16]  G. Freni,et al.  Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas. , 2012, Water science and technology : a journal of the International Association on Water Pollution Research.

[17]  Gabriele Freni,et al.  The Effect of Damage Functions on Urban Flood Damage Appraisal , 2014 .

[18]  Bruce A. Robinson,et al.  Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging , 2007 .

[19]  G. Freni,et al.  Uncertainty in water quality modelling: The applicability of Variance Decomposition Approach , 2010 .

[20]  Michele Mangiameli,et al.  REAL TIME INTEGRATION OF FIELD DATA INTO A GIS PLATFORM FOR THE MANAGEMENT OF HYDROLOGICAL EMERGENCIES , 2014 .

[21]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .