Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning

Jui-Sheng Chou, Chang-Ping Yu, Dinh-Nhat Truong, Billy Susilo, Anyi Hu and Qian Sun
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Jui-Sheng Chou: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Chang-Ping Yu: Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10617, Taiwan
Dinh-Nhat Truong: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Billy Susilo: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Anyi Hu: CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Qian Sun: CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

Sustainability, 2019, vol. 11, issue 24, 1-22

Abstract: 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.

Keywords: microbial community; physicochemical properties; bioclimatic modeling; river environment; multi-output prediction; bio-inspired metaheuristics; optimization; machine learning; data mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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