Narrow Down Forecast Range: Using Knowledge of Past Operations and Attribute-Dependent Thresholding in Good Fishing Ground Prediction

In this study, an attempt has been made to highly narrow down the results of predicting good fishing grounds. In the automatic prediction of good fishing grounds using meteorological and oceanographic information, there is a trade-off between reducing the number of missed detections of good fishing grounds and narrowing down the forecast range. For example, the forecast range will inevitably widen if good fishing grounds are tried to be detected perfectly, which has a negative impact on fishers’ operational decision-making. This study, therefore, attempts to introduce techniques to narrow down the forecast range, such as the use of past operational information and stricter thresholding for judgment, to the convolutional autoencoder-based good fishing ground prediction. Experimental comparisons conducted using actual catch information in bullet tuna trolling demonstrated the effectiveness of the proposed method; it succeeded in narrowing down the forecast range of good fishing grounds without increasing the number of good fishing grounds missed.