Assessing Quality of Water in Taiwan Reservoirs by Machine Learners｜國立臺灣科技大學博碩士論文系統

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研究生：	HA SON HOANG HA SON HOANG
論文名稱：	Assessing Quality of Water in Taiwan Reservoirs by Machine Learners Assessing Quality of Water in Taiwan Reservoirs by Machine Learners
指導教授：	周瑞生 Jui-Sheng Chou
口試委員:	蔡宛珊 Wan-Shan Tsai 廖敏志 Min-Chih Liao 謝佑明 Yu-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：	2017
畢業學年度：	105
語文別：	英文
論文頁數：	165
中文關鍵詞：	Carlson’s Trophic State Index 、Water quality 、Artificial intelligence 、Machine learning 、Data mining 、Meta ensemble 、Nature-inspired metaheuristic regression
外文關鍵詞：	Carlson’s Trophic State Index, Water quality, Artificial intelligence, Machine learning, Data mining, Meta ensemble, Nature-inspired metaheuristic regression
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Water quality is one of the most critical issues in reservoir management owing to its strong effects on the natural environment and human life. This study establishes a machine learning approach for predicting Carlson’s Trophic State Index (CTSI), which is a frequently used metric of water quality in reservoirs. Data collected over ten years (1995-2016) from the stations at 20 reservoirs in Taiwan were preprocessed as the input for the modeling system. Four well-known artificial intelligence techniques, ANNs, SVM, CART, and LR, were used to analyze in baseline and ensemble scenarios. A user-friendly interface that integrates a metaheuristic regression model was developed to evaluate the predictive performance, and to compare it with those in the two constituent scenarios. The comprehensive comparison demonstrated that the ensemble ANN model, based on tiering method, is more accurate than the other single, ensemble models and hybrid metaheuristic regression model. Both the accuracy of prediction and the efficacy of application are considered to support practitioners in planning water management works. Accordingly, this work provides a novel approach for potential use in water quality assessment.

ABSTRACT ........................................................................................................................... i
TABLE OF CONTENTS ..................................................................................................... iv
LIST OF FIGURES ............................................................................................................ vii
LIST OF TABLES ............................................................................................................. viii
ABBREVIATIONS AND SYMBOLS ................................................................................ ix
Chapter 1 Introduction ........................................................................................................... 1
1.1 Research background .............................................................................................. 1
1.2 Research objectives ................................................................................................. 3
1.3 Research process ..................................................................................................... 4
Chapter 2 Literature Review .................................................................................................. 5
2.1 Conventional approaches using diverse water quality indices in the literature....... 5
2.2 Current practice of artificial intelligence in water quality assessment .................... 9
2.3 Proposed approach for assessing water quality ..................................................... 12
Chapter 3 Methodology........................................................................................................ 14
3.1 Baseline models ..................................................................................................... 14
3.1.1 Artificial Neural Networks ............................................................................. 14
3.1.2 Support Vector Machine for classification and regression ............................ 15
3.1.3 Classification and Regression Tree ................................................................ 17
3.1.4 Linear Regression........................................................................................... 18
3.1.5 Software ......................................................................................................... 19
3.2 Ensemble models ................................................................................................... 22
3.2.1 Voting ............................................................................................................. 22
3.2.2 Bagging .......................................................................................................... 23
3.2.3 Stacking .......................................................................................................... 23
3.2.4 Tiering ............................................................................................................ 24
3.2.5 Implemented Software ................................................................................... 25
3.3 Hybrid model ......................................................................................................... 25
3.3.1 Least-Squared Support Vector Machine for Regression ................................ 25
3.3.2 Metaheuristic optimization algorithm ............................................................ 27
3.3.3 Constructing MetaFA based LSSVR model .................................................. 32
3.3.4 Evaluation of optimization algorithm ............................................................ 35
3.3.5 Implemented Software ................................................................................... 37
3.4 Cross-Validation Method ...................................................................................... 37
3.5 Performance evaluation ......................................................................................... 37
3.5.1 Linear correlation coefficient (R)................................................................... 38
3.5.2 Mean Absolute Percentage Error (MAPE) .................................................... 38
3.5.3 Root Mean Squared Error (RMSE) ................................................................ 38
3.5.4 Mean Absolute Error (MAE) ......................................................................... 39
3.5.5 Synthesis Index .............................................................................................. 39
Chapter 4 Water quality data and parameters ...................................................................... 40
4.1 Data ....................................................................................................................... 40
4.2 Parameters description .......................................................................................... 42
Chapter 5 Model development ............................................................................................. 44
5.1 Baseline models ..................................................................................................... 44
5.2 Ensemble models ................................................................................................... 48
5.3 Hybrid model ......................................................................................................... 50
Chapter 6 Experimental results ............................................................................................ 54
6.1 Baseline scenario ................................................................................................... 54
6.2 Ensemble scenario ................................................................................................. 57
6.3 Hybrid scenario ..................................................................................................... 59
6.4 Performance comparison ....................................................................................... 60
Chapter 7 Conclusion ........................................................................................................... 63
References ............................................................................................................................ 66
APPENDIX A. User interface snapshot .............................................................................. 72
A.1 Main user interface of MetaFA-LSSVR system ....................................................... 72
A.2 MetaFA-LSSVR interface (evaluation) .................................................................... 73
A.3 MetaFA-LSSVR interface (prediction)..................................................................... 74
APPENDIX B. Original dataset ........................................................................................... 75
B.1 Original dataset of water quality ............................................................................... 75
APPENDIX C. Performance measure using the baseline models ..................................... 127
C.1 Performance measure using RapidMiner Studio..................................................... 127
C.2 Performance measure using Microsoft Azure Machine Learning Studio ............... 128
C.3 Performance measure using IBM SPSS Modeler ................................................... 129
C.4 Performance measure using WEKA ....................................................................... 130
APPENDIX D. Performance measure using the ensemble models ................................... 131
D.1 Performance measure using voting method ............................................................ 131
D.2 Performance measure using bagging method ......................................................... 132
D.3 Performance measure using stacking method ......................................................... 133
D.4 Performance measure using tiering method ............................................................ 134
APPENDIX E. Performance measure via cross-fold using the hybrid model ................... 135
E.1 Performance measure via cross-fold using hybrid metaheuristic regression model ....................................................................................................................................... 135
APPENDIX F. Analysis report of hybrid metaheuristic regression model ....................... 136
F.1 Analysis report of hybrid metaheuristic regression model ...................................... 136
                                

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