臺灣博碩士論文加值系統

隨著漁船監控系統(Vessel Monitoring System)普遍被應用於各種漁業之漁船，漁船船位資訊被運用於估計努力量、漁獲狀況，並成為漁船管理之利器。我國沿近海漁船數量總數接近二萬艘，其動態不易掌控，自 2007 年農業委員會漁業署要求該等漁船安裝漁船航程記錄器(Voyage Data Recorder, VDR)，得以掌握漁船作業位置。本研究目的希望將龐大的VDR資料，能夠正確轉換為有效努力量，作為漁業動態分析與漁業管理之用。本研究以 2011年臺灣沿海60 艘漁船(包含延繩釣、拖網、扒網等三種漁法)VDR資料庫以及對應之漁獲報表資料，利用類神經網路(Artificial Neural Network, ANN)為分析工具，採用 MATLAB軟體，於樣本船中挑選確實作業之作業時間、日期相對應的VDR 資料庫作訓練資料庫，將速度、航向、加速度、漁業別等列為可能變數，調整神經網路參數(包含神經元數量、訓練次數、均方差極值)以達到性能最佳化，並分析各項漁業的作業模式。主要目標有二，一為判斷各VDR紀錄點的作業狀態，二為判斷各航次的作業漁法。結果顯示在參數上神經元最佳為3個、均方差極值最佳值為0.02，訓練次數(500, 1000, 2000)差異不大。而在判斷作業狀態的結果顯示，各噸級拖網作業狀態的判別率為77%-90%，扒網作業判別率為88%，延繩釣判別率為77%-90%。在各噸級的漁法判別上拖網為69%-91%，扒網為72%，延繩釣為53%-90%，差異最大。使用類神經網路在判別作業點的準確度有不錯的正確率，但在漁法判別出現較大差異。這三種漁業的作業模式分析顯示，延繩釣10噸級以下(CT0, CT1)漁船作業時間比例為67%，10-20噸級(CT2)為60%，拖網10-20噸級為80%、20-50噸級(CT3)為85%、50-100噸級(CT4)為74%，扒網20-50噸級為50%。至於判斷作業狀態部分，CT2以上漁船採用30分鐘一筆，可獲得足夠準確度，且可節省計算時間，CT2以下選擇3分鐘一筆較佳。未來可針對參數進行最佳化設置及取得更準確的資料來源，乃至採用觀察員資料，將可增加辦別的可信度。

Along with the Vessel Monitoring System (VMS) has been implemented in many fisheries, vessels position information were been used in estimating effective effort and catch, and become one of the best tools for vessel management. There are around twenty thousand fishing vessels operating in coastal and offshore waters of Taiwan. It’s difficult to monitor their fishing activities. Since 2007, it is mandatory for those vessels to install Voyage Data Recorder (VDR) under the request of Fisheries Agency, Council of Agriculture. The VDR data provide real fishing positions for those fleets. This research aims to identify the efforts efficiently through VDR data for management purposes. Sixty vessels, including 28 longline,14 trawl vessels, and 8 Taiwanese purse seiners are collected in this research. Their logbooks and VDR data were used. The Matlab is applied for applying ANN (Artificial Neural Network) for identify fishing effort. The fishing time, date and correspondent logbook were selected as training database. The speed, degrees, heading, acceleration, types of fisheries are selected as variables and the parameters (number of neurons, training frequency, mean squared error) would be adjusted to maximize the performance, and explored for the fishing pattern. There are two objectives of this study, first is to identify the vessel operating status and second is to identify the gear types used in each voyage. The results showed the best number of neurons is 3, mean squared error is 0.02, training times (500, 1000, 2000) have no significant differences. The true rates for longline fishery were 77%-90% , 77-90% for the trawl fishery, and 88% for the Taiwanese seine fishery by tonnages. For gear type identification, 52%-90% longline fishery trips, 69%-91% trawl fishery trips, and 72% for Taiwanese seine fishery trips could be identified correctly by tonnages. The results showed the fishing time were 67%, 67%, 60% for for 0-5 tons,5-10 tons, 10-20 tons, longline fishery vessels respectively, and 80%, 85%, 74% for 10-20 tons, 20-50 tons, 50-100 tons trawl vessels respectively. It is suggested to use 30 min frequency data to identifying fishing status for vessels larger than 10-20 tons. It would have good performance and time efficiecny. As for vessels smaller than 10-20 tons, three minute intervals would be necessary. In conclusion, there is good performance for predicting fishing operations with ANN, but in gear identify needs to be improved. Continue to test for best parameters and collect more information, such as observer data could be useful to increase the correct rate.

摘要 I
ABSTRACT II
目錄 III
圖目錄 V
表目錄 VI
第一章 前言 1
第一節 漁船監控系統之發展 1
第二節 研究動機 2
第三節 研究目的 5
第二章 文獻回顧 7
第一節 各國VMS現況 7
第二節 VMS的研究應用 9
第三節 類神經網路之研究 12
第三章 材料與方法 18
第一節 研究對象 18
第二節 資料來源 19
第三節 資料分析 19
第四節 結果判別 23
第五節 應用軟體 24
第四章 結果 25
第一節 基本資料處理 25
第二節 作業判別之最佳參數 26
第三節 漁法判別之最佳參數 28
第四節 作業點判別率 28
第五節 漁法判別率 30
第五章 討論 32
第一節 資料來源 32
第二節 刪除雜訊 32
第三節 類神經網路參數值 33
第四節 作業漁法之特徵 34
第六章 結論與建議 39
第一節 結論 39
第二節 建議 40
參考文獻 41

Arbeláez, F., &; Bouten, I. W.(2005). Applications of Artificial Neural Networks in Ecology.Thesis. Athenaeum Illustre of Amsterdam. Amsterdam.
Bertrand, S., Díaz, E., &; Lengaigne, M. (2008). Patterns in the spatial distribution of Peruvian anchovy (Engraulis ringens) revealed by spatially explicit fishing data. Progress in Oceanography, 79(2), 379-389.
Bigelow, K. A., &; Maunder, M. N. (2007). Does habitat or depth influence catch rates of pelagic species? Canadian Journal of Fisheries and Aquatic Sciences, 64(11), 1581-1594.
Bradshaw, C. J., Davis, L. S., Purvis, M., Zhou, Q., &; Benwell, G. L. (2002). Using artificial neural networks to model the suitability of coastline for breeding by New Zealand fur seals (Arctocephalus forsteri). Ecological Modelling, 148(2), 111-131.
Broomhead, D. S., &; Lowe, D. (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks (No. RSRE-MEMO-4148). Royal signals and radar establishment malvern (United Kingdom).
Bryson, A. E., &; Denham, W. F. (1964). Optimal programming problems with inequality constraints. ii - solution by steepest-ascent. AIAA Journal, 2(1), 25-34.
Chang, S.-K. (2011). Application of a vessel monitoring system to advance sustainable fisheries management—Benefits received in Taiwan. Marine Policy, 35(2), 116-121.
Council of the European (2002). No 2371/2002 of 20 December 2002 on the conservation and sustainable exploitation of fisheries resources under the Common Fisheries Policy. Official Journal of the European Union, L, 358(31.12).
Council of the European (2009). No 1224/2009 of 20 November 2009 establishing a Community control system for ensuring compliance with the rules of the common fisheries policy, amending Regulations (EC) No 847/96,(EC) No 2371/2002,(EC) No 811/2004,(EC) No 768/2005,(EC) No 2115/2005,(EC) No 2166/2005,(EC) No 388/2006,(EC) No 509/2007,(EC) No 676/2007,(EC) No 1098/2007,(EC) No 1300/2008,(EC) No 1342/2008 and repealing Regulations (EEC) No 2847/93,(EC) No 1627/94 and (EC) No 1966/2006. Official Journal of the European Union, L, 343(1), 22-12.
Damanaki, M. (2013a). Negotiations for fisheries agreement protocol with Morocco, EU Press releases. Retrieved 2013.01.11, from http://ec.europa.eu/commission_2010-2014/damanaki/headlines/press-releases/2013/01/20130115-1_en.htm
Damanaki, M. (2013b). New Protocol to the Fisheries Partnership Agreement between the EU and Ivory Coast, EU Press releases. Retrieved 2013.01.09, from http://ec.europa.eu/archives/commission_2010-2014/damanaki/headlines/press-releases/2013/01/20130109-1_en.htm
Dedecker, A. P., Goethals, P. L., Gabriels, W., &; De Pauw, N. (2004). Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium). Ecological Modelling, 174(1), 161-173.
Dinmore, T., Duplisea, D., Rackham, B., Maxwell, D., &; Jennings, S. (2003). Impact of a large-scale area closure on patterns of fishing disturbance and the consequences for benthic communities. ICES Journal of Marine Science: Journal du Conseil, 60(2), 371-380.
Du, W., Deng, J., Han, Y. S., Varshney, P. K., Katz, J., &; Khalili, A. (2005). A pairwise key predistribution scheme for wireless sensor networks. ACM Transactions on Information and System Security (TISSEC), 8(2), 228-258.
Džeroski, S., &; Drumm, D. (2003). Using regression trees to identify the habitat preference of the sea cucumber (Holothuria leucospilota) on Rarotonga, Cook Islands. Ecological Modelling, 170(2), 219-226.
FAO. (2001). International plan of action to prevent, deter and eliminateillegal, unreported and unregulated fishing. FAO. Rome.
FAO. (2014). The status of fishery resources. The State of World Fisheries and Aquaculture.37-41.FAO. Rome.
Gaertner, D., &; Dreyfus-Leon, M. (2004). Analysis of non-linear relationships between catch per unit effort and abundance in a tuna purse-seine fishery simulated with artificial neural networks. ICES Journal of Marine Science: Journal du Conseil, 61(5), 812-820.
Garcia, S. M. (2000). The FAO definition of sustainable development and the Code of Conduct for Responsible Fisheries: an analysis of the related principles, criteria and indicators. Marine and Freshwater Research, 51(5), 535-541.
Gerritsen, H. D., Minto, C., &; Lordan, C. (2013). How much of the seabed is impacted by mobile fishing gear? Absolute estimates from Vessel Monitoring System (VMS) point data. ICES Journal of Marine Science: Journal du Conseil, 70(3), 523-531.
Gerritsen, H.D., &; Lordan, C. (2011). Integrating vessel monitoring systems (VMS) data with daily catch data from logbooks to explore the spatial distribution of catch and effort at high resolution. ICES Journal of Marine Science: Journal du Conseil, 68(1), 245-252.
Gevrey, M., Dimopoulos, I., &; Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249-264.
Gonzalez-Mirelis, G., Lindegarth, M., &; Sköld, M. (2014). Using vessel monitoring system data to improve systematic conservation planning of a multiple-use marine protected area, the Kosterhavet National Park (Sweden). Ambio, 43(2), 162-174.
Hebb, D. O. (2002). The organization of behavior. John Wiley &; Sons.New York.
Hersoug, B., &; Paulsen, O. (1996). Monitoring, control and surveillance in fisheries management. University of Namibia.Windhoek.
Hintzen, N. T., Piet, G. J., &; Brunel, T. (2010). Improved estimation of trawling tracks using cubic Hermite spline interpolation of position registration data. Fisheries Research, 101(1-2), 108-115.
Hintzen, N.T., Bastardie, F., Beare, D., Piet, G. J., Ulrich, C., Deporte, N., Egekvist, J., Degel, H. (2012). VMStools: open-source software for the processing, analysis and visualisation of fisheries logbook and VMS data. Fisheries Research, 115, 31-43.
Hopfield, J. J., &; Tank, D. W. (1985). “Neural” computation of decisions in optimization problems. Biological Cybernetics, 52(3), 141-152.
Jennings, S., &; Lee, J. (2012). Defining fishing grounds with vessel monitoring system data. ICES Journal of Marine Science: Journal du Conseil, 69(1), 51-63.
Joo, R., Bertrand, S., Chaigneau, A., &; Niquen, M. (2011). Optimization of an artificial neural network for identifying fishing set positions from VMS data: an example from the Peruvian anchovy purse seine fishery. Ecological Modelling, 222(4), 1048-1059.
Lambert, G. I., Jennings, S., Hiddink, J. G., Hintzen, N. T., Hinz, H., Kaiser, M. J., &; Murray, L. G. (2012). Implications of using alternative methods of vessel monitoring system (VMS) data analysis to describe fishing activities and impacts. ICES Journal of Marine Science: Journal du Conseil, 69(4), 682-693.
Le Fevre, J. (1987). Aspects of the biology of frontal systems. Advances in Marine Biology, 23, 163-299.
Le Gallic, B., &; Cox, A. (2006). An economic analysis of illegal, unreported and unregulated (IUU) fishing: Key drivers and possible solutions. Marine Policy, 30(6), 689-695.
Lee, J., South, A. B., &; Jennings, S. (2010). Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES Journal of Marine Science: Journal du Conseil, 67(6), 1260-1271.
Linderman, M., Liu, J., Qi, J., An, L., Ouyang, Z., Yang, J., &; Tan, Y. (2004). Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data. International Journal of Remote Sensing, 25(9), 1685-1700.
Makridakis, S. (1993). Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9(4), 527-529.
Mills, C. M., Townsend, S. E., Jennings, S., Eastwood, P. D., &; Houghton, C. A. (2007). Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data. ICES Journal of Marine Science: Journal du Conseil, 64(2), 248-255.
Mullowney, D., &; Dawe, E. (2009). Development of performance indices for the Newfoundland and Labrador snow crab ( Chionoecetes opilio) fishery using data from a vessel monitoring system. Fisheries Research, 100(3), 248-254.
Murawski, S. A., Wigley, S. E., Fogarty, M. J., Rago, P. J., &; Mountain, D. G. (2005). Effort distribution and catch patterns adjacent to temperate MPAs. ICES Journal of Marine Science: Journal du Conseil, 62(6), 1150-1167.
Park, Y.-S., Céréghino, R., Compin, A., &; Lek, S. (2003). Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling, 160(3), 265-280.
Piet, G. J., &; Hintzen, N. T. (2012). Indicators of fishing pressure and seafloor integrity. ICES Journal of Marine Science: Journal du Conseil, 69(10), 1850-1858.
Rijnsdorp, A., Buys, A., Storbeck, F., &; Visser, E. (1998). Micro-scale distribution of beam trawl effort in the southern North Sea between 1993 and 1996 in relation to the trawling frequency of the sea bed and the impact on benthic organisms. ICES Journal of Marine Science: Journal du Conseil, 55(3), 403-419.
Rumelhart, D. E., Hinton, G. E., &; Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
Russo, T., Parisi, A., Prorgi, M., Boccoli, F., Cignini, I., Tordoni, M., &; Cataudella, S. (2011). When behaviour reveals activity: Assigning fishing effort to métiers based on VMS data using artificial neural networks. Fisheries Research, 111(1–2), 53-64.
Smith, R. J., Eastwood, P. D., Ota, Y., &; Rogers, S. I. (2009). Developing best practice for using Marxan to locate marine protected areas in European waters. ICES Journal of Marine Science: Journal du Conseil, 66(1), 188-194.
Tsamenyi, B. M., &; Jaap Molenaar, E. (2000). Satellite-based vessel monitoring systems International legal aspects &; developments in state practice. Organisation FaA, United Nations.
Walker, E., &; Bez, N. (2010). A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data. Ecological Modelling, 221(17), 2008-2017.
Walter, J. F., Hoenig, J. M., &; Gedamke, T. (2007). Correcting for effective area fished in fishery-dependent depletion estimates of abundance and capture efficiency. ICES Journal of Marine Science: Journal du Conseil, 64(9), 1760-1771.
Watson, R., &; Pauly, D. (2001). Systematic distortions in world fisheries catch trends. Nature, 414(6863), 534-536.
Witt, M. J., &; Godley, B. J. (2007). A step towards seascape scale conservation: using vessel monitoring systems (VMS) to map fishing activity. PLoS One, 2(10), e1111.
WWF-Australia. (2012). Review of the Fisheries Management Act 1991 and Fisheries Administration Act 1991. Sydney.
尹相志 (2009). SQL Server 2008 Data Mining 資料採礦. 悅知文化.臺北.
王勝平 (2007). VMS應用在沿近海漁業資源管理模式之研究. 行政院農委會漁業署委託計畫.
王富鈺 (2013). 臺灣北部海域漁業時空分析.碩士論文,國立臺灣海洋大學海洋事務與資源管理研究所,基隆.
行政院農委會漁業署 (2013). 中華民國台閩地區漁業統計年報. 行政院農委會漁業署.
李國添 (1999). 本省漁船在東、黃海域作業(拖網除外)情形調查分析.行政院農委會漁業署委託計畫.
周耀烋和蘇偉成 (2002). 臺灣漁具漁法: 行政院農業委員會漁業署.臺北.
林忠宏 (2013). 節省公帑、社會正義、漁業發展 漁船航程資訊系統(Voyage Data Recorder). 國立成功大學校刊 (243) 39-41.
邱宜賢 (2007). 調整漁船用油優惠政策之執行成效. 農政與農情, 183, 49-52。
柯慶麟 (2010). 20噸以上未滿100噸延繩釣漁船裝設漁船監控系統輔導措施簡介. 農政與農情, 218, 28-30.
徐鉦忠 (2010). 臺灣北部沿近海延繩釣漁業之產能與經營效益評估. 碩士論文,國立臺灣海洋大學海洋事務與資源管理研究所,基隆.
張淑淨 (2013). 漁船監控系統.科技大觀園. 科技部. Retrieved 2014.09.18, from http://scitechvista.most.gov.tw/zh-tw/Articles/C/0/1/10/1/1958.htm.
張詠棨 (2007). 半徑基底函數(RBF)類神經網路應用於LED晶圓缺陷檢測.碩士論文,國立雲林科技大學資訊管理系,雲林.
張裕明 (2001). 連續表面波試驗及電子震測錐試驗評估土層剪力波速─倒傳遞類神經網路.碩士論文,國立臺灣大學土木工程學研究所,臺北.
許鎦響和萬絢 (2007). 整合霍普菲爾分群技術與疊代式決策樹分析高單價化妝品的潛在顧客. 計量管理期刊, 4, 119-132。
陳奇中 (2009). MATLAB在化工上之應用: 東華出版社,臺北.
陳彥君 (2011). 利用航程記錄器和漁獲日誌資料探討臺灣沿近海延繩釣漁業之作業模式.碩士論文,國立臺灣海洋大學海洋事務與資源管理研究所,基隆.
曾千芬 (2009). 應用地理資訊系統探討東港拖網漁業之漁獲組成及時空特性.碩士論文,國立臺灣海洋大學環境生物與漁業科學學系,基隆.
黃華山 (2005). 類神經網路預測臺灣 50股價指數之研究.碩士論文,國立彰化師範大學資訊管理學系,彰化.
楊荏婷 (2013). 談海洋資源的永續發展策略. 國政研究報告.財團法人國家政策研究基金會.
葉怡成 (2009). 類神經網路模式式應用與實作: 儒林圖書有限公司.臺北.
廖怡婷 (2009). 運用標本船動態紀錄分析漁業資源變動-以貢寮地區火誘網為例.碩士論文,國立臺灣海洋大學環境生物與漁業科學學系,基隆.
管振青 (2003). 臺灣沿近海漁業減船政策之效益評估.碩士論文,國立中山大學經濟學研究所,高雄.
劉坤玉和張水鍇 (1998). 漁船監控系統之發展概況. 農政與農情, 74(311), 32-36.
鄭奕、方水美和周應祺. (2008). 中國近海捕撈能力的計量分析. 漁業論壇暨現代農業與食品經濟國際學術研討會論文集, 上海.
賴繼昌、洪銘昆、楊清閔、黃建智和吳龍靜 (2012). 臺灣沿近海底拖網漁船航跡資料自動化系統架設與應用. 水試專訊(39), 1-4.
謝邦昌和邱志洲 (2000). 類神經網路分析: 曉園出版社有限公司.臺北.
謝雅吟 (2009). 利用船位及漁撈日誌資料分析臺灣西南海域中小型雙拖網漁業活動之特性.碩士論文,國立臺灣海洋大學環境生物與漁業科學學系,基隆.
蘇木春和張孝德 (2004). 機器學習: 類神經網路, 模糊系統以及基因演算法則: 全華科技圖書公司.新北.