Particle filter-based data assimilation technique for the evaluation of transport of pollutants in small rivers

Abstract

The discharge of pollutants into water resources has caused many undesirable effects on human health and the environment. In this context, meta-heuristic methods, such as the Simulated Annealing algorithm, allow for only offline estimation of the concentration and dispersion coefficient of pollutant. However, online data assimilation is a very important for decision support in real time. In this regard, the present paper focuses on the use of the so-called Particle Filter as an online observer. Our studies were performed through the algorithms Sampling Importance Resampling (SIR) and Liu and West (LW), using tracer experimental observations from a small river in Brazil. Such approach allowed for tracking satisfactorily the pollutant concentration, longitudinal dispersion coefficient, flow velocity, pollutant load released and distance from the pollution source. The results can be enhanced ever more through the LW filter, which reduces the impoverishment of the samples as well as the computational time due to the use of a smoothing kernel.