Use of a fuzzy qualitative model to reanalyze radon relationship with atmospheric variables in a coastal area near a NORM repository

A soft computing methodology, PreFuRGe (Predictive Fuzzy Rules Generator), was applied to study atmospheric radon in relation to other meteorological variables. This algorithm is based on fuzzy clustering and generates a rule-based system that provides a qualitative model that describes the behavior of radon. Two one-year radon datasets were used to verify the consistency of the fuzzy methodology, by comparing the obtained models with published results obtained with statistical techniques. These datasets were collected in the city of Huelva, to the southwest of Europe, and were already studied in peer-reviewed publications. The proposed fuzzy methodology was able to provide results consistent with previous studies, identifying the main drivers in radon behavior, atmospheric stability and wind speed, together with a complete characterization of the daily cycle and its seasonality. PreFuRGe highlighted features that would otherwise require detailed examination combining a number of classical statistical techniques, proving to be a useful tool to characterize heterogeneous datasets. (C) 2022 The Author(s). Published by Elsevier B.V.

Automated summary

This study applied a soft computing methodology, PreFuRGe, to analyze the relationship between atmospheric radon and other meteorological variables. The methodology utilized fuzzy clustering and generated a rule-based system to provide a qualitative model for describing radon behavior. By comparing the results with published studies using statistical techniques, the consistency of the fuzzy methodology was verified.