Seawater intrusion pattern recognition supported by unsupervised learning: A systematic review and application

FlowSOM clustering - A novel pattern recognition approach for water research: Application to a hyper-arid coastal aquifer system

Monitoring and understanding of water resources have become essential in designing effective and sustainable management strategies to overcome the growing water quality challenges. In this context, the utilization of unsupervised learning techniques for evaluating environmental tracers has facilitated the exploration of sources and dynamics of groundwater systems through pattern recognition. However, conventional techniques may overlook spatial and temporal non-linearities present in water research data. This paper introduces the adaptation of FlowSOM, a pioneering approach that combines self-organizing maps (SOM) and minimal spanning trees (MST), with the fast-greedy network clustering algorithm to unravel intricate relationships within multivariate water quality datasets. By capturing connections within the data, this ensemble tool enhances clustering and pattern recognition. Applied to the complex water quality context of the hyper-arid transboundary Caplina/Concordia coastal aquifer system (Peru/Chile), the FlowSOM network and clustering yielded compelling results in pattern recognition of the aquifer salinization. Analyzing 143 groundwater samples across eight variables, including major ions, the approach supports the identification of distinct clusters and connections between them. Three primary sources of salinization were identified: river percolation, slow lateral aquitard recharge, and seawater intrusion. The analysis demonstrated the superiority of FlowSOM clustering over traditional techniques in the case study, producing clusters that align more closely with the actual hydrogeochemical pattern. The outcomes broaden the utilization of multivariate analysis in water research, presenting a comprehensive approach to support the understanding of groundwater systems.

Identification of water pollution sources and analysis of pollution trigger conditions in Jiuqu River, Luxian County, China

Against the backdrop of ecological conservation and high-quality development in the Yangtze River Basin, there is an increasing demand for enhanced water pollution prevention and control in small watersheds. To delve deeper into the intricate relationship between pollutants and environmental features, as well as explore the key factors triggering pollution and their corresponding warning thresholds, this study was conducted along the Jiuqu River, a strategically managed unit in the upstream region of the Yangtze River, between 2022 and 2023. A total of seven monitoring sites were established, from which 161 valid water samples were collected. The k-nearest neighbors mutual information (KNN-MI) technique indicated that water temperature (WT) and relative humidity (RH) were the main environmental factors. The principal component analysis (PCA) of ten water quality parameters and three environmental factors unveiled the distinguishing characteristics of the primary pollution sources. Consequently, the pollution sources were categorized as treated wastewater > groundwater runoff > phytoplankton growth > abstersion wastewater > agricultural drainage. Furthermore, the regression decision tree (RDT) algorithm was used to explore the combined effects between pollutants and environmental factors, and to provide visual decision-making process and quantitative results for understanding the triggering mechanism of organic pollution in Jiuqu River. It conclusively identifies total phosphorus (TP) as the predominant triggering parameter with the threshold of 0.138 mg/L. The study is helpful to deal with potential water pollution problems preventatively and shows the interpretability and predictive performance of the RDT algorithm in water pollution prevention.

Research on Seawater Intrusion Suppression Scheme of Minjiang River Estuary

Seawater intrusion in the Minjiang River estuary has gravely endangered the water security of the surrounding area in recent years. Previous studies mainly focused on exploring the mechanism of intrusion, but failed to provide a scheme for suppressing seawater intrusion. The three most relevant determinants to chlorine level, which represented the strength of seawater intrusion, were determined using Pearson correlation analysis as being the daily average discharge, daily maximum tidal range, and daily minimum tidal level. Considering the lower requirement of sample data and the ability to handle high-dimensional data, the random forest algorithm was used to construct a seawater intrusion suppression model and was combined with a genetic algorithm. The critical river discharge for suppressing estuary seawater intrusion determined using this model. The critical river discharge was found to gradually increase with the maximum tidal range, which in three different tide scenarios was 487 m³/s, 493 m³/s, and 531 m³/s. The practicable seawater intrusion suppression scheme was built up with three phases to make it easier to regulate upstream reservoirs. In the scheme, the initial reading of river discharge was 490 m³/s, and it rose to 650 m³/s over six days, from four days before the high tide's arrival to two days following it, and before falling down to 490 m³/s at the end. Verified with the 16 seawater intrusion events in the five dry years, this scheme could eliminate 75% of the seawater intrusion risk and effectively reduce the chlorine level for the remaining 25% of events.