Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo. (January 2018)

• Record Type: Journal Article
• Authors: Abbaszadeh, Peyman; Moradkhani, Hamid; Yan, Hongxiang
• Is Part Of: Advances in water resources. Volume 111(2018)
• Page Start: 192
• Page End: 204
• Publication Date: 2018-01
• Subjects: Particle Filters -- Markov Chain Monte Carlo -- Genetic algorithm -- Hydrologic prediction; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2017.11.011
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: A new data assimilation technique based on Particle Filter, Genetic algorithm and MCMC is proposed. The approach considerably enhances the effectiveness and robustness of data assimilation. The particle degeneracy and sample impoverishment in particle filtering is considerably reduced with small ensemble size. Abstract: Particle Filters (PFs) have received increasing attention by researchers from different disciplines including the hydro-geosciences, as an effective tool to improve model predictions in nonlinear and non-Gaussian dynamical systems. The implication of dual state and parameter estimation using the PFs in hydrology has evolved since 2005 from the PF-SIR (sampling importance resampling) to PF-MCMC (Markov Chain Monte Carlo), and now to the most effective and robust framework through evolutionary PF approach based on Genetic Algorithm (GA) and MCMC, the so-called EPFM. In this framework, the prior distribution undergoes an evolutionary process based on the designed mutation and crossover operators of GA. The merit of this approach is that the particles move to an appropriate position by using the GA optimization and then the number of effective particles is increased by means of MCMC, whereby the particle degeneracy is avoided and the particle diversity is improved. In this study, the usefulness and effectiveness of the proposed EPFM is investigated by applying the technique on a conceptual and highly nonlinear hydrologic model over four river basins located in different climate and geographical regions of the United States. Both synthetic and real case studies demonstrate that the EPFM improves both the state and parameter estimation more effectively and reliably as compared with the PF-MCMC.