Application of artificial neural networks to the geochemical study of an impacted fluvial system

In this paper we illustrate how self organizing maps (SOM) can be used to assess the influence of natural and anthropogenic factors in an impacted fluvial system. We use the Growing Cell Structures (GCS) algorithm to study a multivariable data set that includes the chemical composition of 90 stream sediments, collected along the Rapel Fluvial System, in Central Chile. The GCS algorithm, an extension of SOM, allows simultaneous adaptation of the position of the map pointers in the input space and the topology of the output space. Therefore it helps to find visual representations of geochemical data that simplify the analysis and yet allow all relevant information to be preserved. The results show that the data can be separated into a limited number of groups with clear and different chemical characteristics. Each group corresponds to stream sediments that are restricted to a specific portion of the fluvial system. In the upper part of the catchment, the Cachapoal River is characterized by elevated Cu-Mo-As-Sb concentrations that reflect input of mining-derived material, coupled with high B values associated with agro-industrial activities. In turn, the high P concentrations of the Tinguiririca River reflect an extensive use of phosphates in its floodplain. Due the influence of the Rapel Lake, the lower part of the catchment is mainly controlled by natural factors such as downstream changes in the lithologycal composition of the bedrock and in the hydrodynamic conditions of the Rapel River. Comparisons are made between the results obtained by using GCS and SOM. Both techniques enable the recognition of the cluster structure and their chemical characteristics. However, GCS requires a simpler map structure with less number of units, which optimizes translating the relevant information to geographical maps and make associations with spatially distributed features such as bedrock geology, urbanization, industrial activities and land use.