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Open AccessArticle

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

1
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
2
Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10617, Taiwan
3
CAS Key Laboratory of Urban Pollutant Conversion, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(24), 6889; https://doi.org/10.3390/su11246889
Received: 23 October 2019 / Revised: 25 November 2019 / Accepted: 2 December 2019 / Published: 4 December 2019
(This article belongs to the Special Issue Project Intelligence and Management)
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. View Full-Text
Keywords: microbial community; physicochemical properties; bioclimatic modeling; river environment; multi-output prediction; bio-inspired metaheuristics; optimization; machine learning; data mining microbial community; physicochemical properties; bioclimatic modeling; river environment; multi-output prediction; bio-inspired metaheuristics; optimization; machine learning; data mining
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Chou, J.-S.; Yu, C.-P.; Truong, D.-N.; Susilo, B.; Hu, A.; Sun, Q. Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning. Sustainability 2019, 11, 6889.

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