Author(s): Jucheng Ren Wei Wu

Linked Author(s):

Keywords: Kriging surrogate; Differential evolution adaptive metropolis optimization; Sudden water pollution; Identify the pollution source;

Abstract: For sudden water pollution incidents in river, the ability to quickly identify the pollution source is of great importance for early accident warning and emergency control. In this study, on the basis of bayesian reasoning, a method for kriging surrogate-- DREAM optimization coupling traceability is set up in the posterior space of pollution sources. By this method, the surrogate model established by using the kriging method is generated based on the Latin hypercube sampling and the high precision hydrodynamic -- water quality numerical calculation. To reduce the uncertainty of the surrogate model and improve the efficiency of source identification, this paper integrates the kriging surrogate- DREAM optimization process, and according to the mean square error criterion, it insertes new samples adaptive to improving the precision of surrogate model in the posterior space on the pollution sources, forming a more efficient computing model to determine source location and releasing time and released mass for sudden water pollutant in river. In order to further eliminate errors, the algorithm also adopts a two-stage manner to identify pollution source information as a whole, that is, the surrogate model is used to fully search, with the high precision hydrodynamic--water quality model used to trace the source. The proposed approach is tested by numerical and engineering examples. The result show that the new approach can effectively improve the calculation efficiency, satisfy the identification of source information for sudden water pollution incidents in rivers, and can be used for the identification source information and emergency response in river .

DOI: https://doi.org/10.3850/38WC092019-1388

Year: 2019