用网站访问数据预测垂钓者出没，准确率达78%。
Website visits can predict angler presence using machine learning
- 用钓鱼平台网站访问量做输入，训练机器学习模型预测船只数量。
- 在已知湖上模型预测准确率达R2=0.77，未知湖仅R2=0.21。
- 无需复杂数据，网站访问量即可高效预测垂钓活动，适合管理决策。
了解并预测休闲垂钓者的努力程度对可持续渔业管理至关重要。传统测量方法如调查成本高且时空覆盖有限。基于环境或经济因素的预测模型通常依赖历史数据，因数据稀缺而限制了其时空泛化能力。本研究利用安大略省加拿大近200个湖泊五年间在线钓鱼平台的高分辨率数据及易获取的辅助数据，预测每日船隻出现情况和空中计数。仅依靠湖信息网站访问量，即可实现每日垂钓船只存在预测准确率达78%。虽加入环境、社会生态、天气及钓手报告特征后对船只存在预测提升不显著，但显著提升了船只数量预测效果。在训练包含的湖泊上模型最高达到R2=0.77，但在未知湖泊上表现较差（R2=0.21）。结果表明，整合在线钓鱼平台数据可有效增强预测模型，为渔业管理提供新工具。
原文摘要 · Abstract (English)
Understanding and predicting recreational angler effort is important for sustainable fisheries management. However, conventional methods of measuring angler effort, such as surveys, can be costly and limited in both time and spatial extent. Models that predict angler effort based on environmental or economic factors typically rely on historical data, which often limits their spatial and temporal generalizability due to data scarcity. In this study, high-resolution data from an online fishing platform and easily accessible auxiliary data were tested to predict daily boat presence and aerial counts of boats at almost 200 lakes over five years in Ontario, Canada. Lake-information website visits alone enabled predicting daily angler boat presence with 78% accuracy. While incorporating additional environmental, socio-ecological, weather and angler-reported features into machine learning models did not remarkably improve prediction performance of boat presence, they were substantial for the prediction of boat counts. Models achieved an R2 of up to 0.77 at known lakes included in the model training, but they performed poorly for unknown lakes (R2 = 0.21). The results demonstrate the value of integrating data from online fishing platforms into predictive models and highlight the potential of machine learning models to enhance fisheries management.
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