A least squares method for identification of unknown groundwater pollution source

Abstract

The identification of unknown groundwater pollution sources is one of the most important premises in groundwater pollution prevention and remediation. In this paper, an exploratory application of a least squares method to identify the unknown groundwater pollution source is conducted. Supported by a small amount of observation data and the analytical solutions of the pollutant transport model, the initial concentration, the leakage location and the pollutant mass are identified by using the least squares method under a sand tank experiment and a gas station area. In the sand tank experiment, it is found that the fitting errors of three cross-sections are within 6%. In the gas station area, it is found that the results are nearly consistent with the site investigation information. The results indicate that the least squares method has considerable application values in the identification of groundwater pollution sources.

Robust Hybrid Classification Methods and Applications

“…The least-square approach (LSA), like other estimation procedures and its variant, has received extensive coverage (He et al, 2021;Drygas, 2021;Yao et al, 2020;Kern, 2016;Miller, 2006). This unique technique can be traced to Galton (1886) though coined by Legendre in 1800s.…”

“…It is also applied to determine the line of best fit of the given data set. The LSA has been applied to provide solutions to a power line (Girshin et al, 2016), pressure detection (Sun et al, 2015), motor induction (Koubaa, 2006), data fitting (Chen & Liu, 2012), and identification of groundwater pollution (He et al, 2021). On the other hand, the LPA has been applied to solve different problems, including the travel time and modeling the prediction of Covid-19 outbreak (Ogundokun et al, 2020;Olarenwaju & Harrison, 2020), climate change (Hasselman & Barnett, 1981) and data forecasting (Vaseghi, 2008).…”