The use of the self-organizing map (SOM) methodology to study the dissolved organic matter (DOM) in different water body types

Abstract

This study investigated the characteristics of dissolved organic matter (DOM) in two distinct water bodies, through the utilization of three-dimensional fluorescence spectroscopy coupled with self-organizing map (SOM) methodology. Specifically, this analysis concentrated on neurons 3, 14, and 17 within the SOM model, identifying notable differences in the DOM compositions of a coal subsidence water body (TX) and the MaChang Reservoir (MC). The humic substance content of DOM TX exceeded that of MC. The origin of DOM in TX was primarily linked to agricultural inputs and rainfall runoff, whereas the DOM in MC was associated with human activities, displaying distinctive autochthonous features and heightened biological activity. Principal component analysis revealed that humic substances dominated the DOM in TX, while the natural DOM in MC was primarily autochthonous. Furthermore, a multiple linear regression model (MLR) determined that external pollution was responsible for 99.11% of variation in the humification index (HIX) of water bodies. HIGHLIGHTS Study of the variation in DOM spectral characteristics of different waterbodies.; Neural networks were applied to spectral resolution.; Source analysis was performed to identify the origin of water pollution.;

Keywords

• absolute principal component-multiple linear regression
• dissolved organic matter
• fluorescence spectroscopy
• machine learning
• non-point source pollution
• self-organizing map