Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.

ABSTRACT: We investigate methods that improve the estimation of indoor gamma ray dose rates at locations where measurements had not been made. These new predictions use a greater range of modelling techniques and larger variety of explanatory variables than our previous examinations of this subject. Specifically, we now employ three types of machine learning models in addition to the geostatistical, nearest neighbour and other earlier models. A large number of parameters, mostly describing the characteristics of dwellings in the area in question, have been added to the set of explanatory variables. The use of machine learning methods results in significantly improved predictions over earlier models. The machine learning models are noisy and there is some instability in the relative importance of par

SUBMITTER: Kendall GM 

PROVIDER: S-EPMC11810612 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature