# Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
###### Abstract
[PERSON] et al. [3] analyzed various hydrological attributes to assess the groundwater susceptibility to pollution in northern Pakistan. The results showed that almost 60% of the area has a high to moderate vulnerability to groundwater contamination. However, a similar study outlining the nitrate concentration locations has not been reported to date for Pakistan. The assessment of groundwater vulnerability and its nitrate contamination risk can aid water management authorities in performing the appropriate nitrate risk mitigation procedures. Knowing the effects of groundwater vulnerability in areas where it is used as the public water supply source is essential to groundwater safety. There are dual risks to groundwater (over-extraction and adverse land use), therefore necessitating a dual nature of safety [18]. The risk assessment is not the same as the assessment for groundwater's vulnerability to pollution. The idea of groundwater vulnerability is to assess the vulnerability of groundwater resources to being badly impacted by an enforced contaminant load from the surface [19]. There are two types of groundwater vulnerability, namely intrinsic and specific vulnerability. The ease with which a contaminant from the surface can reach and diffuse in groundwater can be specified as intrinsic vulnerability [20]. In comparison, specific vulnerability is exploited to ascertain the vulnerability of groundwater to an individual contaminant or a group of contaminants and their association [21]. The concept of vulnerability helps to identify groundwater regions in areas that are at high risk based on their vulnerability, so as to adopt appropriate risk management options. Moreover, it also reduces the communication space between the decision-makers, planners, and hydrogeologists, and helps in water management. Several approaches have been utilized to assess the level of vulnerability and contamination of groundwater around the globe. These include index techniques, interpolation techniques, process-based models, and statistical models. In the index techniques, experts suggest a weight to each parameter in associated methods based on their knowledge. These techniques include the DRAV model [22], Susceptibility Index (SI) technique [23], GOD technique [24], ANIMO and EPIC models [25], and the DRASTIC model [2]. The interpolation methods utilize sensors and devices and indicator kriging (IK) based on geostatistical procedures using the kriging interpolation algorithm [26; 27; 28; 29]. These are used to assess the groundwater pollution risk with ease. However, this method is constrained by the requirement of dense data collection points, and associated output uncertainty. The process-based models are relatively complex, for example, the groundwater loading effects of agricultural management systems (GLEAMS) [30; 31; 32; 33], the pesticide root zone model (PRZM-3) [30; 31], the water flow and nitrate transport global model (WNGM) [34; 35], and the groundwater flow model (MODFLOW) [36]. The critical drawbacks of these approaches are the important input data [37], and the restricted local scales applicability [38; 39]. The statistical models follow a statistical linear and nonlinear regression approach [40]. These approaches model the contamination by analyzing the association between contaminant density and the specific contamination influencing parameters [41]. To entail causality, expert knowledge is required for expressive forecasts where reaching a consensus may present challenges. Recently, the use of soft computing approaches such as artificial intelligence and several machine learning (ML) techniques have been considerably applied for predicting risks and hazards in environmental sciences [1; 42; 43; 44; 45; 46; 47]. ML, an application of artificial intelligence (AI), allows systems to automatically learn and improve from experience without being explicitly programmed [48]. It focuses on developing computer programs that can access data, and use it to learn for themselves.