Self-Organizing Map Applications in Groundwater Quality Assessment
Summary
Self-Organizing Maps (SOMs) have emerged as a powerful unsupervised neural-network technique for exploring complex groundwater quality datasets. By projecting high-dimensional water chemistry variables onto a two-dimensional grid, SOMs reveal intrinsic patterns in space and time without requiring predefined classes. When integrated with clustering algorithms or combined with principal component analysis, they can delineate hydrogeochemical facies, identify the principal drivers of contamination and distinguish natural geochemical processes from anthropogenic influences. Such insights support targeted monitoring, inform remediation strategies and guide sustainable groundwater management across diverse climatic and geological settings.
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Self-Organizing Map Applications in Groundwater Quality Assessment publication trend
The graph below shows the total number of articles in self-organizing map applications in groundwater quality assessment across all publications each year (not limited to Nature Index journals).
Technical terms
Self-Organizing Map (SOM): An unsupervised artificial neural network that organises and visualises high-dimensional data by projecting similar input patterns onto neighbouring nodes in a low-dimensional grid.
Hydrogeochemical Facies: Distinctive water-type classifications based on major ion composition, reflecting dominant geochemical reactions such as silicate weathering or carbonate dissolution.
Clustering: A data-analysis process that groups samples according to similarity in multiple variables, often used post-SOM to define discrete zones or pollution sources.
Principal Component Analysis (PCA): A statistical method that reduces dataset dimensionality by transforming variables into a smaller set of uncorrelated components, facilitating pattern recognition.
References
- Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review. The Science of The Total Environment (2023).
- Evaluating Spatiotemporal Variations of Groundwater Quality in Northeast Beijing by Self-Organizing Map. Water (2020).
- Characterizing land use effect on shallow groundwater contamination by using self-organizing map and buffer zone. The Science of The Total Environment (2021).
- Application of a Self-Organizing Map of Isotopic and Chemical Data for the Identification of Groundwater Recharge Sources in Nasunogahara Alluvial Fan, Japan. Water (2020).
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