Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain)

Samper-Pilar, Javier; Samper-Calvete, Javier; Mon, Alba; Pisani, Bruno; Paz-González, Antonio

doi:10.3390/hydrology12030049

Open AccessFeature PaperEditor’s ChoiceArticle

Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain)

by

Javier Samper-Pilar

¹,

Javier Samper-Calvete

^1,*

,

Alba Mon

¹

,

Bruno Pisani

¹

and

Antonio Paz-González

²

¹

Interdisciplinary Center of Chemistry and Biology (CICA), Civil Engineering, Campus de Elviña, University of A Coruña, 15071 A Coruña, Spain

²

Interdisciplinary Center of Chemistry and Biology (CICA), Facultad de Ciencias, University of A Coruña, 15071 A Coruña, Spain

^*

Author to whom correspondence should be addressed.

Hydrology 2025, 12(3), 49; https://doi.org/10.3390/hydrology12030049

Submission received: 31 January 2025 / Revised: 3 March 2025 / Accepted: 4 March 2025 / Published: 6 March 2025

(This article belongs to the Special Issue Novel Procedures and Methodologies for Surface and Underground Water Quality Analysis: Theory and Application)

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Abstract

The Abelar pilot basin in Coruña (northwestern Spain) has been monitored for hydrological and hydrochemical data to assess the effects of eucalyptus plantation and manure applications on water resources, water quality, and nitrate contamination. Here, we report the machine learning analysis of hydrological and hydrochemical data from the Abelar basin. K-means cluster analysis (CA) is used to relate nitrate concentrations at the outlet of the basin with daily interflows and groundwater flows calculated with a hydrological balance. CA identifies three linearly separable clusters. Times series Gaussian process regression (TS-GPR) is employed to predict surface water nitrate concentration by incorporating hydrological variables as additional input parameters using a time series shifting. TS-GPR allows modelling nitrate concentrations based on shifted interflows and groundwater flows and chemical concentrations with R² = 0.82 and 0.80 for training and testing, respectively. Groundwater flow from five days prior to the current date,

Q_{g 5}

, is the most important input parameter of the TS-GPR model. Interaction effects between the variables are found. TS-GPR validation with recent data provides results consistent with those of testing (R² = 0.85). Model inspection by permutation feature importance and partial dependence plots shows interactions between

Q_{g 5}

and Cl, and between Ca and Mg.

Keywords: K-means clustering; Gaussian process regression; time series analysis; Abelar pilot basin; nitrate concentration; applied machine learning; hydrological data; hydrochemical data

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MDPI and ACS Style

Samper-Pilar, J.; Samper-Calvete, J.; Mon, A.; Pisani, B.; Paz-González, A. Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain). Hydrology 2025, 12, 49. https://doi.org/10.3390/hydrology12030049

AMA Style

Samper-Pilar J, Samper-Calvete J, Mon A, Pisani B, Paz-González A. Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain). Hydrology. 2025; 12(3):49. https://doi.org/10.3390/hydrology12030049

Chicago/Turabian Style

Samper-Pilar, Javier, Javier Samper-Calvete, Alba Mon, Bruno Pisani, and Antonio Paz-González. 2025. "Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain)" Hydrology 12, no. 3: 49. https://doi.org/10.3390/hydrology12030049

APA Style

Samper-Pilar, J., Samper-Calvete, J., Mon, A., Pisani, B., & Paz-González, A. (2025). Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain). Hydrology, 12(3), 49. https://doi.org/10.3390/hydrology12030049

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain)

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