Work

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

Year: 2025

Type: article

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 modeling 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, Qg5, 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 Qg5 and Cl, and between Ca and Mg.

Source: Hydrology

DOI: 10.3390/hydrology12030049

Authors: Javier Samper-Pilar, Javier Samper-Calvete, Alba Mon, Bruno Pisani, Antonio Paz-Gonzalez

Institution: Universidade da Coruña

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Life on land

Open Access status: gold

Estimated APC paid: $2,172