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

Abstract

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

Keywords

• agricultural activity
• nitrate
• groundwater contamination
• machine learning
• Naïve-Bayes
• Decision-tree