Groundwater pollution source identification using Metropolis-Hasting algorithm combined with Kalman filter algorithm

Author:

Luo JiannanORCID,Li XueliORCID,Xiong Yu,Liu Yong

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference43 articles.

1. An improved Bayesian approach linked to a surrogate model for identifying groundwater pollution sources;An;Hydrgeol. J.,2022

2. Urban water flow and water level prediction based on deep learning;Assem;Machine Learn. Knowledge Disc. Databases,2017

3. A linked simulation–optimization model for solving the unknown groundwater pollution source identification problems;Ayvaz;J. Contam. Hydrol.,2010

4. Probabilistic simulation for reliable solute source identification in heterogeneous porous media;Bagtzoglou,1991

5. Application of particle methods to reliable identification of groundwater pollution sources;Bagtzoglou;Water Resour. Manag.,1992

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