Machine Learning as a Diagnosis Tool of Groundwater Quality in Zones with High Agricultural Activity (Region of Campo de Cartagena, Murcia, Spain)

Groundwater is humanity’s freshwater pantry, constituting 97% of available freshwater. The 6th Sustainable Development Goal (SDG) the UN Agenda 2030 promotes “Ensure availability and sustainable management water sanitation for all”, which takes special significance in arid or semi-arid regions. region Campo de Cartagena (Murcia, Spain) has one most technified productive irrigation systems Europe. As a result, groundwater this zone serious chemical quality problems. To qualify predict region, may later facilitate its management, two machine learning models (Naïve-Bayes Decision-tree) are proposed. These did not require great computing power were developed from reduced number data using KNIME (KoNstanz Information MinEr) tool. Their accuracy was tested by corresponding confusion matrix, providing high both models. obtained results showed that higher northern west zones. This be due to presence north Andalusian aquifer, deepest Cartagena, predominance rainfed crops, where amount leaching fertilizers lower, coming mainly rainfall.