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研究生： 福 倪
Fini Iuni
論文名稱： 利用卷積長短期記憶模型(ConvLSTM)預測吐瓦魯經濟海域之鮪魚蹤跡
Predicting Tuna Fishing Locations in Tuvalu’s Exclusive Economic Zone Using Convolutional Long Short-Term Memory Network
指導教授： 黃能富
Huang, Nen-Fu
口試委員: 吳庭育
Wu, Tin-Yu
石維寬
Shih, Wei-Kuan
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊系統與應用研究所
Institute of Information Systems and Applications
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 94
中文關鍵詞： ConvLSTM 、吐瓦魯 、鮪魚 、海表溫度 、葉綠素a 、專屬經濟區
外文關鍵詞： ConvLSTM, Tuvalu, Tuna, Sea Surface Termperature, Chlorophyll a, Exclusive Economic Zone
相關次數： 點閱：39  下載：0 
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    • 這項研究專注於使用兩種先進模型來預測吐瓦魯專屬經濟區（EEZ）內的金槍魚捕撈點：棲息地適宜指數（HSI）和卷積長短期記憶（ConvLSTM）模型。HSI模型整合了環境因素，如海表溫度（SST）、葉綠素濃度和捕撈努力。儘管HSI模型與金槍魚出現的正相關性較弱，但其結果在統計上具有顯著性，證明其在預測適宜棲息地方面的有效性。

    ConvLSTM模型能夠高精度地捕捉空間和時間模式，顯著超越多種基準集成學習模型。其卓越的性能突顯了其在預測金槍魚出現方面的穩健性和有效性。

    該研究強調了HSI和ConvLSTM模型在吐瓦魯EEZ內的漁業管理和保護方面的潛力。未來的研究可以通過引入更多數據源來進一步提升這些模型，從而促進可持續的漁業管理和海洋生態系統的保護。

    This study focuses on predicting tuna fishing spots within Tuvalu’s Exclusive Economic Zone (EEZ) using two advanced models: the Habitat Suitability Index (HSI) and the Convolutional Long Short-Term Memory (ConvLSTM). The HSI model integrates environmental factors such as sea surface temperature (SST), chlorophyll concentration, and fishing efforts. Despite a weak positive correlation with tuna presence, the HSI model’s results were statistically significant, demonstrating its effectiveness in predicting suitable habitats.

    The ConvLSTM model captures both spatial and temporal patterns with high accuracy, significantly outperforming several benchmarked ensemble learning models. Its superior performance highlights its robustness and effectiveness in predicting tuna presence.

    The study underscores the potential of HSI and ConvLSTM models for fisheries management and conservation within Tuvalu’s EEZ. Future research could further enhance these models by incorporating additional data sources, contributing to sustainable fisheries management and the preservation of marine ecosystems.

    Introduction .................................................... 1 1.1 Research Background .............................. 3 1.1.1 Tuvalu .............................................. 3 1.1.2 Tuna ................................................. 6 1.1.3 Habitat Suitability Index Model ..... 10 1.1.4 Convolutional Long Short-Term Memory (ConvLSTM) ......... 11 1.2 Research Motivation ............................... 12 1.3 Research Objectives ............................... 13 Related Work ................................................ 15 2.1 Tuna and Oceanographic .......................... 16 2.2 Vessel Monitoring System ..................... 19 2.3 Habitat Suitability Index (HSI) .............. 20 2.4 Convolutional Long Short-Term Memory .................... 22 2.5 Integration of ConvLSTM and HSI ........ 24 Methodology .............................................. 27 3.1 Data Collection .................................. 28 3.1.1 Fishing Effort Data ...................... 29 3.1.2 Sea Surface Temperature (SST) Data ............................ 29 3.1.3 Chlorophyll a Concentration ...... 30 3.2 Pre-processing ................................... 31 3.2.1 Fishing Efforts ............................. 31 3.2.2 Sea Surface Temperature ............ 32 3.2.3 Chlorophyll ................................. 33 3.2.4 Merging Sea Surface Temperature, Chlorophyll and Fishing Effort ...... 34 3.3 Habitat Suitability Index Model ........ 35 3.4 Validation of HSI Score .................... 38 3.4.1 Pearson Correlation .................. 39 3.4.2 Chi-Square Test ......................... 40 3.4.3 Spatio-Temporal Logistic Regression Analysis ..................... 40 3.4.4 HSI Performance Metrics .......... 41 3.4.5 HSI Data Visualization ............. 43 3.5 Convolutional Long Short Term Memory Model ..................... 44 3.5.1 Data Preparation for ConvLSTM ... 44 3.5.2 ConvLSTM Formula .................. 45 3.5.3 ConvLSTM Design .................... 48 3.5.4 Training Process ......................... 50 3.5.5 ConvLSTM Performance Metrics .................................. 50 3.6 Benchmark ........................................ 51 3.6.1 Random Forest ........................... 51 3.6.2 Gradient Boosting ....................... 51 3.6.3 AdaBoost .................................. 52 3.6.4 XGBoost ................................... 52 3.6.5 Support Vector Regression (SVR) .................. 52 Data Analysis .............................................. 53 4.1 Habitat Suitability Index Result ........ 53 4.2 Validation of HSI Score .................... 55 4.2.1 Pearson Correlation Analysis .... 55 4.2.2 Chi-square Test Result .............. 56 4.2.3 Spatiotemporal Logistic Regression Analysis .............................. 56 4.2.4 HSI Performance Metric ............. 57 4.2.5 HSI Data Visualization .............. 57 4.3 ConvLSTM ......................................... 59 4.3.1 Training Process .......................... 59 4.3.2 ConvLSTM Performance Metrics ............................. 63 4.3.3 ConvLSTM Data Visualization ................................... 63 4.4 Benchmark Result ............................... 65 4.4.1 Random Forest Performance ...... 66 4.4.2 Gradient Boosting Performance .. 66 4.4.3 AdaBoost Performance ............... 66 4.4.4 XGBoost Performance ................ 67 4.4.5 SVR with PCA Performance ...... 67 4.4.6 Benchmark Summary ................. 67 Discussion .................................................. 69 5.1 Habitat Suitability Index (HSI) ......... 69 5.2 ConvLSTM Model ........................... 71 Conclusion ................................................. 75 Future Work ............................................. 77 7.1 Working with a Finer Resolution ..... 77 7.2 Implementing the ConvLSTM Model in a Real-Time Monitoring System .......... 78 7.2.1 Data Integration and Real-Time Processing ............................ 78 7.2.2 Model Optimization for Real-Time Predictions ........................... 78 7.2.3 Deployment in Portable Systems .......................... 79 7.2.4 Management and Decision Support .......................... 79

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