## 1 Introduction  
Water, a rare and vulnerable resource, is the main environmental issue all over the world. The scarcity and continuous deterioration of this resource has triggered researchers to adopt a huge variety of modern investigation techniques for its protection to avoid uncensored overexploitation and its anarchic management [1]. Groundwater is essential for agricultural and industrial developments; however, the rapid extension of irrigated areas, along with population growth and industrial development, has stressed the quantitative and qualitative status of this precious resource [2]. Due to the growing issue related to water resources, people in developed countries have finally begun to realize that it is necessary to change the bad practices surrounding this resource and to implement a coherent policy for rational groundwater management [3], as also remarked on by the SDGs proposed by the United Nations. In particular, the sixth SDG states the importance of ensuring \"Clean water and sanitation\", highlighting the primary importance of protecting and restoring water-related ecosystems. The lack of water in arid and semi-arid regions is becoming a major constraint for local and regional sustainable development. Moreover, arid and semi-arid regions are also characterized by significant spatiotemporal imbalances between water demand and water supply, making the implementation of new strategies extremely necessary for the implementation of integrated and sustainable groundwater resources management [4]. Today, vulnerability maps are widely used for groundwater management and protection [5; 6]. These maps are generally established based on geological, pedological, hydrological and hydrogeological factors. Moreover, is possible to distinguish two types of vulnerability: (i) intrinsic vulnerability linked to the geological environment and local climatic conditions, and (ii) specific vulnerability linked to the properties of the contaminant/pollutant. The first groundwater vulnerability maps were established by researchers in the early 1970s [7]. Afterwards, they were then positively tested in other countries around the world. The principle of their elaboration consisted in carrying out the synthesis of a few parameters influencing the aquifer's vulnerability. In general, groundwater vulnerability maps can be grouped into four major categories: (1) overlay and index methods, (2) statistical methods, (3) modeling-based methods [8; 9; 10; 11], and (4) recently introduced hybrid methodologies. The latter represent a combination of the above-mentioned categories aimed to increase the reliability of groundwater vulnerability results. They involve the hybridization of rating methodologies with statistical and mathematical models aimed to increase the reliability of results [12; 13; 14]. However, as a drawback, these new methods become extremely site-specific and data-dependent. Generally, rating and overlay methods are the most widely utilized, and several methods have been proposed and tested in the last 30 years, such as: DRASTIC [15], GOD [16], AVI [17], PRAST [18], ISIS [19], SINTACS [20], EPIK [21], SI [22], RISKE [23], PI [24], and GALDIT [25]. Among these, the DRASTIC method, along with GOD, has been applied in areas with different climates and hydrogeologic characteristics with good results. Moreover, its results are easily understood by water managers. However, these methodologies only offer a static representation of vulnerability, making their hybridization mandatory to ensure proper management as well as enabling short-term vulnerability forecasting. Several modifications have been proposed in the last years: [PERSON] et al. [14] achieved the optimization of rating methods combining statistical analysis with parameter objectivation; [PERSON] et al. [26] enhanced DRASTIC performance using artificial intelligence; and [PERSON] et al. [27] combined DRASTIC and MODFLOW to increase vulnerability performance. A complete review of all methodologies and modifications is available in [PERSON] et al.