دورية أكاديمية
Comparison of Machine Learning Models in the Prediction of Accumulation of Heavy Metals in the Tree Species in Kanchipuram, Tamilnadu
| العنوان: | Comparison of Machine Learning Models in the Prediction of Accumulation of Heavy Metals in the Tree Species in Kanchipuram, Tamilnadu |
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| المؤلفون: | R. Sumathi and G. Sriram |
| المصدر: | Nature Environment and Pollution Technology, Vol 22, Iss 2, Pp 853-860 (2023) |
| بيانات النشر: | Technoscience Publications, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Environmental effects of industries and plants LCC:Science (General) |
| مصطلحات موضوعية: | machine learning, heavy metals, accumulation, plant species, prediction, Environmental effects of industries and plants, TD194-195, Science (General), Q1-390 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 0972-6268 2395-3454 |
| Relation: | https://neptjournal.com/upload-images/(27)B-3976.pdf; https://doaj.org/toc/0972-6268; https://doaj.org/toc/2395-3454 |
| DOI: | 10.46488/NEPT.2023.v22i02.027 |
| URL الوصول: | https://doaj.org/article/484558cef8be421899ab46d177917de2 |
| رقم الأكسشن: | edsdoj.484558cef8be421899ab46d177917de2 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 09726268 23953454 |
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| DOI: | 10.46488/NEPT.2023.v22i02.027 |