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研究生： 關華明
Billy Susilo
論文名稱： Predicting Bacteria in Freshwater based on Environmental Factors by Machine Learning
Predicting Bacteria in Freshwater based on Environmental Factors by Machine Learning
指導教授： 周瑞生
Jui-Sheng Chou
口試委員: 蔡完珊
Christina Tsai
于昌平
Chang-Ping Yu
謝佑明
Yo-Ming Hsieh
周瑞生
Jui-Sheng Chou
學位類別： 碩士
Master
系所名稱： 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 96
中文關鍵詞： bacteria community 、environmental factors 、bioclimatic modeling 、multi-output prediction 、artificial intelligence 、machine learning 、data mining
外文關鍵詞： bacteria community, environmental factors, bioclimatic modeling, multi-output prediction, artificial intelligence, machine learning, data mining
相關次數： 點閱：455  下載：0 
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    • The main goal of microbial ecology is to understand the relationship between earth’s microbial community and the environment. This study develops a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the bacteria species in freshwater as a function of environmental factors. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of environmental attributes from the river in Southeast China. An 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 bacteria species for further experimental planning with minimal expenditure, which in present day become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming scenario. This work presents a neoteric approach for potential use in predicting microbial structures in the environment.

    The main goal of microbial ecology is to understand the relationship between earth’s microbial community and the environment. This study develops a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the bacteria species in freshwater as a function of environmental factors. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of environmental attributes from the river in Southeast China. An 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 bacteria species for further experimental planning with minimal expenditure, which in present day become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming scenario. This work presents a neoteric approach for potential use in predicting microbial structures in the environment.

    ABSTRACT i TABLE OF CONTENTS iv LIST OF FIGURES vii LIST OF TABLES viii ABBREVIATIONS AND SYMBOLS ix 1. Introduction 1 1.1. Research background 1 1.2. Research objectives 3 1.3. Research process 4 1.4. Research importance and contributions 4 2. Literature Review 6 2.1. Ecological informatics 6 2.2. Bacterial community in the river 7 2.3. Environmental and deoxyribose nucleic acid sequencing factor 9 2.4. Current practice of artificial intelligence to predict bacteria community 12 3. Methodology 14 3.1. Data pre-processing 16 3.1.1. Dimensionality reduction 16 3.1.2. Handling of missing data 17 3.2. Hybrid of multi-output model and optimization algorithm 23 3.2.1. Multi-Output Least Square Support Vector Regression 24 3.2.2. Accelerated Particle Swarm Optimization 26 3.3. Performance evaluation 27 4. Data Collections 30 5. Model Development 34 5.1. Determining critical factors related to bacteria in a river 34 5.2. Complementing bacteria community data model development and evaluation 37 5.3. Hybrid model development 42 6. Experimental Results 44 7. Conclusions 48 References 50 APPENDIX A. Original Input Attributes 57 APPENDIX B. Original Output Attributes 58 APPENDIX C. Dimensionality Reduction Stage I 59 APPENDIX D. Dimensionality Reduction Stage II 60 APPENDIX E. Dimensionality Reduction Stage III 61 APPENDIX F. Dimensionality Reduction Stage IV 62 APPENDIX G. Bacteria Datasets Input Attributes 63 APPENDIX H. Bacteria Datasets Output Attributes 66 APPENDIX I. Score Box of Dimensionality Reduction Stage II 69 APPENDIX J. Dissolved Oxygen Attribute Predicted Value 70 APPENDIX K. Tutorial of Dimensionality Reduction Implementation in IBM-SPSS Software 71 APPENDIX L. Optimized Machine Learning Procedure Implemented in MATLAB 75

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