## 1 Introduction
Several approaches have been proposed for the determination of rating scores such as AHP, statistical techniques, and artificial intelligence methods [26]. Amongst these approaches, a method based on simple statistical procedures is prominent [12; 22]. This method revises the rating score according to the mean nitrate concentration of every range determined by [PERSON]'s DRASTIC model for each parameter, which is simple and statistically reclassifies the ranges into groups with different concentrations based on data obtained from the Wilcoxon Rank Sum Test. In previous studies, the modification of the DRASTIC method generally included the following procedures: (1) modification of the rating scores for each parameter, (2) modification of the factor weights, and (3) addition and subtraction of parameters based on the correlation between the parameter and contaminate concentration [6]. However, the revisions of the rating scores and weights are interrelated. The modification of the rating scores will inevitably affect the modification of the weights. If the rating scores have been determined, the corresponding weights have theoretically also been determined. At present, the weights are determined by either averaging the heterogeneity of hydrogeological parameters or by considering the correlation between a single parameter and the concentration of the pollutant, ignoring the correlation between parameters, and therefore are not necessarily applicable to various hydrogeological settings. The accuracy of vulnerability mapping is also important for the evaluation of the effectiveness of the assessment methods. In previous studies, correlation coefficients between the degree of vulnerability and concentration of specific contaminants have been used to verify vulnerability maps [2; 12]. Another method is based on the difference between groundwater vulnerability and nitrate concentration levels [27]. Both methods can be used to effectively verify the consistency of vulnerability assessment results with the current pollution situation. In other words, these methods are suitable for the verification of static vulnerability assessment results. In several studies, it has been pointed out that the time trend should be considered for the groundwater vulnerability [28; 29; 30; 31]. However, from a dynamic point of view, it needs to be verified whether the vulnerability assessment results reflect long-term changes in the pollution conditions. In this paper, a novel data-driven weight determination method based on the correlation between the parameters and pollutant concentration using Monte Carlo (MC) or genetic algorithm (GA) methods is developed. And a new method for the verification of the feasibility and accuracy of the groundwater-specific vulnerability on a temporal scale has also been proposed. These methods will be introduced and compared to DRASTIC's common, pesticide, and modified versions with respect to the assessment of the groundwater vulnerability for the Lake Baiyangdian watershed in the North China Plain (NCP). The objectives of this study were twofold: (1) to identify a common methodology for weights that is both objective and applicable to a wide range of hydrogeological situations, and (2) to propose programs that can verify that the results of the vulnerability assessment reflect long-term changes in the state of contamination.