Deep Learning-Based Detection of Seawater Intrusion Using Multivariate Hydrogeochemical Data

Seawater intrusion is a groundwater salinization process caused by seawater influx into coastal aquifers, and its severity has increased due to sea-level rise associated with climate change and excessive groundwater extraction. Previous studies on seawater intrusion detection have primarily relied on chemical indicators or fixed threshold values; however, these approaches have limitations in accounting for interactions among multivariate water quality data and variations in hydrogeochemical characteristics. This study aimed to develop a model for detecting seawater intrusion-affected samples using machine learning and deep learning techniques. In this study, data from the National Groundwater Monitoring Network in Korea were collected to define the background characteristics of domestic freshwater groundwater. The dataset consisted of 16 variables, including 13 original water quality parameters (electrical conductivity (EC), Na, Mg, K, Ca, Cl, SO₄, HCO₃, pH, Fe, Mn, NO₃ and dissolved oxygen (DO)) and 3 derived variables reflecting the geochemical characteristics of seawater intrusion (Na/Cl ratio, Ca/Mg ratio, and Base Exchange Index; BEX). These data were used to train a Variational Autoencoder (VAE), a deep learning-based generative model, which compressed the data into a 4-dimensional latent space. To quantify the degree of differentiation from freshwater according to seawater mixing ratios, synthetic data were generated by coupling PHREEQC with R to incorporate key geochemical reaction mechanisms associated with seawater intrusion, including cation exchange reactions during seawater-freshwater mixing. Anomaly detection techniques were then applied to evaluate detection performance. The results demonstrated that samples could be distinguished from the freshwater distribution even at low seawater mixing ratios, suggesting the potential for determining minimum detectable contamination levels for seawater intrusion monitoring. This study presents a novel approach for seawater intrusion detection based on machine learning and deep learning, and is expected to contribute to early detection of seawater intrusion and sustainable management of coastal groundwater resources.