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) 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.

Datos y Recursos

Cite como

García-del-Toro E.M. García-Salgado S. Mateo L.F. Quijano M.A. y Más-López M.I. Machine Learning as a Diagnosis Tool of Groundwater Quality in Zones with High Agricultural Activity (Region of Campo de Cartagena Murcia Spain). MDPI, 2022. https://doi.org/10.3390/agronomy12123076

Clipboard Icon
Recuperado: 19 Jan 2025 18:00:53
RIS BibTeX

Metadatos
Descargar
DCAT-AP/NTI-RISP

Información básica
Tipo de recurso Artículo
Fecha de creación 05-11-2024
Fecha de última modificación 19-01-2025
Mostrar histórico de cambios
Identificador de los metadatos f96c0403-9154-5cf6-b1db-f866f0985371
Idioma de los metadatos Español
Temáticas (NTI-RISP)
Categoría del conjunto de alto valor (HVD) Observación de la Tierra y medio ambiente
Categoría temática ISO 19115 biota
URI de palabras clave
Información bibliográfica
Nombre del autor García-del-Toro, E.M., García-Salgado, S., Mateo, L.F., Quijano, M.A. y Más-López, M.I.
Nombre del editor MDPI
Identificador alternativo DOI: 10.3390/agronomy12123076
Identificador del autor
Email del autor evamaria.garcia@upm.es
Web del autor
Procedencia
Declaración de linaje
Perfil de Metadatos
Notas sobre la versión
Versión