Assessment of parameter uncertainty for non-point source pollution mechanism modeling: A Bayesian-based approach.

Uncertainty assessment of parameters associated with non-point source pollution mechanism modeling are crucial for improving the effectiveness of pollution controlling. In this study, an approach based on Bayesian inference and integrated Markov chain Monte Carlo and multilevel factorial analysis has been developed, and it can not only apply straightforward Bayesian inference to assess parameter uncertainties, but also quantitatively investigate the main and interactive effects of multiple parameters on the model response variables by measuring the specific variations of model outputs. Its applicability and advantages are presented through the application of the Soil and Water Assessment Tool to Shitoukoumen Reservoir Catchment in northeast China.

Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty.

The simulation-optimization method is widely used in the design of the groundwater pollution monitoring network (GPMN). The uncertainty of the simulation model will significantly affect the design results of GPMN. When the Monte Carlo method is used to consider the influence of model uncertainty on the optimization results, the simulation model needs to be invoked many times, which will cause a huge amount of calculation.

Hydrogeochemical processes identification and groundwater pollution causes analysis in the northern Ordos Cretaceous Basin, China.

It is necessary to identify the hydrogeochemical processes and analyze the causes of groundwater pollution due to the lack of knowledge about the groundwater chemical characteristics and the endemic diseases caused by groundwater pollution in the northern Ordos Cretaceous Basin. In this paper, groundwater chemical facies were obtained using the piper trilinear diagram based on the analysis of 190 samples. The hydrogeochemical processes were identified using ionic ratio coefficient, such as leaching, evaporation and condensation.

Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method-A Case Study of Western Jilin Province.

This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables.