Joo, R., Bertrand, S., Chaigneau, A., & Ñiquen, 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.
摘要
目前大規模的船隊活動可透過漁船監控系統追蹤查詢，這些漁船位置被期待可用於提升漁獲努力量的估計或管理。但無法提供漁船是否進行捕撈的實際訊息。VMS數據通常只依據簡單的標準進行檢測(例如速度之閾值)，並都集中在檢測真陽性上，並不重視估計誤差。在秘魯鯷魚捕撈的案例中，這些標準高估了總量的182%。為了解決這個問題，本研究將介紹類神經網路(ANN)的方法。為設置ANN參數的最佳化，透過敏感度分析進行幾項優化:(1)內部結構及演算法培訓(2)培訓演算法所需資料庫大小與組成之選取規則。優化後的ANN可改善捕撈位置與數量之估計。在本研究中，ANN減少了1%的估計誤差並獲得了76%的真陽性。儘管是使用於秘魯鯷魚漁業數據，但這種ANN可根據VMS和觀察員的資料運用在各種漁業上，具有廣泛的潛在價值。為了提高ANN結果的準確率，本研究也提出了一些改善利用觀察員和VMS數據抽樣設計的建議。
ABSTRACT:
The spatial behavior of numerous fishing fleets is nowadays well documented thanks to satellite Vessel Monitoring Systems. Vessel positions are recorded on a frequent and regular basis which opens promising perspectives for improving fishing effort estimation and management. However, no specific information is provided on whether the vessel is fishing or not. To answer that question, existing works on VMS data usually apply simple criteria (e.g. threshold on speed). Those simple criteria generally focus in detecting true positives; conversely, estimation errors are given no attention. For our case study, the Peruvian anchovy fishery, those criteria overestimate the total number of fishing sets by 182%. To overcome this problem an artificial neural network (ANN) approach is presented here. In order to set both the optimal parameterization and use “rules” for this ANN, we perform an extensive sensitivity analysis on the optimization of (1) the internal structure and training algorithm of the ANN and (2) the “rules” used for choosing both the relative size and the composition of the databases (DBs) used for training and inferring with the ANN. The “optimized” ANN greatly improves the estimates of the number and location of fishing events. For our case study, ANN reduces the total estimation error on the number of fishing sets to 1% (in average) and obtains 76% of true positives. While fitted on Peruvian anchovy fishery data, this type of neural network approach has wider potential and could be implemented in any fishery relying on both VMS and at-sea observer data. In order to increase the accuracy of the ANN results, we also suggest some criteria for improving sampling design by at-sea observers and VMS data.
