Integration of geoelectric and geochemical data using Self-Organizing
  Maps (SOM) to characterize a landfill
Camila Juliao Johan Diaz Yosmely BermÚdez Milagrosa Aldana
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.