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系統識別號 U0026-0401202001085200
論文名稱(中文) 整合遙測與機器學習於多時間尺度魚場預測
論文名稱(英文) Integration of Remote Sensing and Machine Learning for Multi-timescale Fishing Area Determination
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 108
學期 1
出版年 109
研究生(中文) 艾迪拉
研究生(英文) Adillah Alfatinah
電子信箱 adillah.alfatinah@gmail.com
學號 P66077026
學位類別 碩士
語文別 英文
論文頁數 127頁
口試委員 指導教授-朱宏杰
口試委員-張懿
口試委員-陳一菁
中文關鍵字 none 
英文關鍵字 Skipjack Tuna  Remote Sensing  Environmental Parameters  Decision Tree (DT)  Generalized Linear Model (GLM)  Catch Per Unit Effort (CPUE) 
學科別分類
中文摘要 none
英文摘要 Skipjack tuna was one of the most important and significant catches of fish in the world. That makes maintaining their existences critically important to human communities that rely on them for food and economic well-being, particularly at a time of global ocean change. The tuna distribution in broad geographic areas and high market value have gradually supported the political and economic importance of the various commercial activities related to this species group. Predicting a specific fish area for this species can help to optimize the catch and help make the monitoring more accessible. Studies about predicting fish area were commonly done by investigating the relation between skipjack tuna life preference with the marine environmental variables, such as Chlorophyll-a, Sea Surface Temperature, and so on. Satellite imagery often employed to obtain the value of these environmental variables due to its ability to monitor a large area and provide much more information than it would be possible to get solely from the surface.
This study used skipjack tuna catch data in the year 2017 and utilized Chlorophyll-a, Sea Surface Temperature (SST), and Sea Surface Height (SSH) as the environmental variables. Chlorophyll-a and SST was obtained from MODIS-Aqua Level 3 SMI while SSH was obtained from Global Ocean Analysis. Following the catch data, all the environmental data were from the year 2017. This study considered three multi-scale cases distinguished by temporal processing. The Case I processed with the yearly data in 2017, Case II processed seasonal data, and Case III processed weekly data. Two methods (Decision Tree, DT and Generalized Linear Model, GLM) were employed for predicting skipjack area. DT achieved mostly above 80% accuracy rate. Overall, based on the accuracy assessment, this study concluded that DT appears to perform better than GLM in predicting skipjack tuna fish area. Furthermore, this study also predicted the fish catch per unit effort (CPUE) using regression tree and GLM, both models were appropriate for prediction. Meanwhile, GLM (average RMSE is 11.452) performed slightly better than the regression tree (average RMSE is 10.878) in the monthly. Moreover, the most influenced environmental variable in both model construction was SST, which means the existence of skipjack tuna in one region is affected mostly by the regional temperature.
論文目次 ABSTRACT ii
ACKNOWLEDGMENT iv
TABLE OF CONTENT v
LIST OF FIGURES viii
LIST OF TABLES xii
CHAPTER 1. INTRODUCTION 1
CHAPTER 2. LITERATURE REVIEW 5
2.1 Skipjack Tuna Presence Relation with Environmental Parameter 5
2.2 Utilization of Satellite Imagery for Fish Prediction 6
2.3 Related Works Regarding to Fishing Area Prediction 8
CHAPTER 3. MATERIAL AND METHODS 11
3.1 Study Area & Cases 11
3.1.1 Study Area 11
3.1.2 Study Case 12
3.2 Data Collection 13
3.2.1 Fishing Catch Points 14
3.2.2 Environmental Factors Satellite Imagery 19
3.3 Workflow 22
3.4 Fish Presence Area Prediction Processing 27
3.4.1 Fishing Catch Points Density Map 27
3.4.2 Training and Testing Samples Dataset Preparation 28
3.4.3 Fishing Area Prediction Model Construction 29
3.4.4 Accuracy Assessment 32
3.4.5 Prediction Model Selection for Weekly Processing 34
3.5 Catch Per Unit Efforts Prediction Processing 35
3.5.1 Catch Per Unit Efforts Calculation 35
3.5.2 Training and Testing Samples Dataset Preparation 36
3.5.3 CPUE Prediction Model Construction 36
3.5.4 Root Mean Square Error (RMSE) Calculation 38
CHAPTER 4. RESULTS AND DISCUSSION 39
4.1 Fishing Catch Points Density Maps 39
4.2 Fish Presence Probability Map 43
4.2.1 Fish Presence Probability Map Predicted by DT 44
4.2.2 Fish Presence Probability Map Predicted by GLM 54
4.3 Fish Presence Area Maps 61
4.3.1 Fish Presence Area Maps Predicted by DT 61
4.3.2 Fish Presence Area Maps Predicted by GLM 66
4.4 Accuracy Assessment 70
4.4.1 Accuracy Assessment for Case I 70
4.4.2 Accuracy Assessment for Case II 71
4.4.3 Accuracy Assessment for Case III 72
4.5 Model Prediction Selection Based on the Environmental Variables Correlation 74
4.6 CPUE Value Prediction 77
4.6.1 CPUE Prediction from Regression Tree 77
4.6.2 CPUE Prediction from GLM 83
4.6.3 RMSE Calculation 88
4.7 Discussion 96
4.7.1 Model Construction & Performances 96
4.7.2 Spatial Distribution of Predicted Skipjack Tuna Fish Area 99
4.7.3 Temporal Change in Environmental Parameter 107
CHAPTER 5. CONCLUSION 114
5.1 Conclusion 114
5.2 Future Works 116
REFERENCES 118
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