Integration of geoelectric and geochemical data using Self-Organizing Maps (SOM) to characterize a landfill

TL;DR

This study combines geoelectric and geochemical data using Self-Organizing Maps to effectively delineate leachate-affected zones around a landfill, enhancing environmental monitoring and risk assessment.

Contribution

It introduces an integrated approach using SOMs to classify and visualize landfill contamination zones based on multiple geophysical and geochemical variables.

Findings

Identified specific zones with high risk of leachate contamination.

Generated contour maps correlating anomalies with landfill parameters.

Demonstrated effective delimitation of affected areas.

Abstract

Leachates from garbage dumps can significantly compromise their surrounding area. Even if the distance between these and the populated areas could be considerable, the risk of affecting the aquifers for public use is imminent in most cases. For this reason, the delimitation and monitoring of the leachate plume are of significant importance. Geoelectric data (resistivity and IP), and surface methane measurements, are integrated and classified using an unsupervised Neural Network to identify possible risk zones in areas surrounding a landfill. The Neural Network used is a Kohonen type, which generates; as a result, Self-Organizing Classification Maps or SOM (Self-Organizing Map). Two graphic outputs were obtained from the training performed in which groups of neurons that presented a similar behaviour were selected. Contour maps corresponding to the location of these groups and the individual variables were generated to compare the classification obtained and the different anomalies associated with each of these variables. Two of the groups resulting from the classification are related to typical values of liquids percolated in the landfill for the parameters evaluated individually. In this way, a precise delimitation of the affected areas in the studied landfill was obtained, integrating the input variables via SOMs. The location of the study area is not detailed for confidentiality reasons.