Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques

Abstract

The presence and persistence of pesti-cides in the environment are environmental problems of great concern due to the health implications for humans and wildlife. The persistence of DDT–DDE in a Mediterranean coastal plain where pesticides were widely used and were banned decades ago is the aim of this study. Different sources of analytical information from water and soil analysis and topogra-phy and geographical variables were combined with the purpose of analyzing which environmental factors are more likely to condition the spatial distribution of DDT–DDE in the drainage watercourses of the area. An approach combining machine learning techniques, such as Random Forest and Mutual Information (MI), for classifying DDT–DDE concentration lev-els based on other environmental predictive variables was applied. In addition, classification procedure was iteratively performed with different training/valida-tion partitions in order to extract the most informa-tive parameters denoted by the highest MI scores and larger accuracy assessment metrics. Distance to drain canals, soil electrical conductivity, and soil sand tex-ture fraction were the most informative environmen-tal variables for predicting DDT–DDE water concen-tration clusters.