Comparison of Machine Learning Models in the Prediction of Accumulation of Heavy Metals in the Tree Species in Kanchipuram, Tamilnadu

Abstract

Arsenic, aluminum, iron, lead, chromium, copper, zinc, manganese, and cadmium are some of the heavy metal pollutants in the air that cause severe impacts on the biotic and abiotic environment. This study intended to find the accumulation capacity of the heavy metals on the leaves of tree species such as Terminalia catappa, Syzygium cumini, Saraca asoca, Pongamia glabra, and Ficus religiosa and predict their accuracy by comparing different machine learning (ML) models. The samples were collected at six different locations (likely Vellagate, Cancer Institute, CSI hospital area, Moongilmandapam, Collectorate, and Pallavarmedu) and distributed in a manner within Kanchipuram town, Tamil Nadu, in February and March of 2018 and 2019, respectively. Six ML methods were selected, such as KStar (K*), Lazy IKB, Logistic Regression Algorithm (LR), LogitBoost Classifier (LB), Meta Randomizable Filtered Classifier (MRFC), and Random Tree (RT), for prediction and to compare the efficiency of their predictions. Out of six models, Logistic functions perform well in terms of TP rate when compared to other classifiers (93.21%-99.81% TPR– 0.93–0.99) and Logitboost attained a low TP rate that ranged from 0.76 to 0.82. This study indicates the feasibility of different ML methods in the prediction of species capabilities toward the accumulation of heavy metals.