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系統識別號 U0026-2301201913444400
論文名稱(中文) 特徵選取方法用於估計日本霞浦湖葉綠素 a 之濃度
論文名稱(英文) Feature selection for estimation of chlorophyll-a concentration in Kasumigaura Lake, Japan.
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 107
學期 1
出版年 108
研究生(中文) 阮文孟
研究生(英文) Manh Nguyen Van
學號 P66067063
學位類別 碩士
語文別 英文
論文頁數 59頁
口試委員 共同指導教授-朱宏杰
召集委員-蔡榮得
指導教授-林昭宏
口試委員-Jaelani Lalu Muhamad
中文關鍵字 None 
英文關鍵字 feature selection  water quality  chlorophyll-a  remote sensing. 
學科別分類
中文摘要 None
英文摘要 Healthy inland freshwater sources, such as lakes, reservoirs, rivers, and streams, play crucial roles in providing numerous benefits to surrounding societies. However, these inland water bodies, named case-II waters, have been severely polluted by human activities. Therefore, long-term monitoring and real-time measurements of water quality are essential to identify the changes of water quality for unexpected environmental incidents avoidance.
Over last 40 years, satellite-based remote sensing techniques, which have become powerful tools, enable researchers to efficiently monitor water quality of large-scale waterbodies. The success of satellite-based water quality studies relies on three key components: precise atmospheric correction method, optimization algorithm, and regression model. Previous studies integrated various algorithms and regression models, including (semi-) empirical or (semi-) analytical algorithms, and (non-) linear regression models, to obtain satisfactory results. Nevertheless, the selection of appropriate algorithm is complex and challenging because of the fact that the changes in chemical and physical properties of water can lead to different method determination. Ultimately, an accurate correction for atmospheric effects, especially in turbid productive case-II waters, which plays as important pre-processing step, is not always fully considered.
To alleviate the aforementioned difficulties, this study proposed a potential integration which comprises an optimization method for efficient water-quality model selection, ordinary least squares regression, and an accurately atmospheric corrected dataset. Prime focus of this study is water-quality model selection which optimizes an objective function that aims to maximize prediction accuracy of regression models.
According to the experiments, the performance of the selected water-quality model using proposed procedures, dominated that of the existing algorithms in terms of root-mean-squared-error (RMSE), normalized-mean-absolute-error (NMAE), the Pearson correlation coefficient (r), and slope of the regressed line (m) between measured and predicted chlorophyll-a.
論文目次 ABSTRACT i
ACKNOWLEDGEMENT iii
CONTENTS vi
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 STUDY AREA 8
CHAPTER 3 MATERIALS 12
CHAPTER 4 METHODOLOGY 18
CHAPTER 5 RESULTS AND DISCUSSIONS 35
CHAPTER 6 CONCLUSIONS 52
References 54
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