https://scholars.lib.ntu.edu.tw/handle/123456789/546771
標題: | Predicting microbial species in a river based on physicochemical properties by bio-inspired metaheuristic optimized machine learning | 作者: | Chou, J.-S. Yu, C.-P. Truong, D.-N. Susilo, B. Hu, A. Sun, Q. CHANG-PING YU |
關鍵字: | Bio-inspired metaheuristics; Bioclimatic modeling; Data mining; Machine learning; Microbial community; Multi-output prediction; Optimization; Physicochemical properties; River environment | 公開日期: | 2019 | 卷: | 11 | 期: | 24 | 來源出版物: | Sustainability (Switzerland) | 摘要: | The main goal of the analysis of microbial ecology is to understand the relationship between Earth's microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the microbial species in a river as a function of physicochemical parameters. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of physicochemical attributes from a river in Southeast China. A bio-inspired metaheuristic optimized machine learner, which supports the adjustment to the multiple-output prediction form, is used in bioclimatic modeling. The accuracy of prediction and applicability of the model can help microbiologists and ecologists in quantifying the predicted microbial species for further experimental planning with minimal expenditure, which is become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming. This work demonstrates a neoteric approach for potential use in predicting preliminary microbial structures in the environment. © 2019 The Author(s). |
URI: | https://www.scopus.com/inward/record.url?eid=2-s2.0-85079428237&partnerID=40&md5=ec92724c1f9341515dce1d6289ea378a https://scholars.lib.ntu.edu.tw/handle/123456789/546771 |
DOI: | 10.3390/su11246889 | SDG/關鍵字: | artificial intelligence; bioclimatology; climate modeling; community structure; data mining; environmental conditions; machine learning; microbial community; microbial ecology; optimization; physicochemical property; prediction; China |
顯示於: | 環境工程學研究所 |
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