Fish Catch Prediction by Combining Fishing, Weather and Tidal Data

Tomohiro Tanaka, Yasuyuki Tahara, Akihiko Ohsuga, Yuichi Sei

2025

Abstract

This study presents a model designed to predict days with increased probabilities of fish catches for inexperienced anglers by utilizing weather and tidal data. Specifically, the study pre-processed catch data, together with meteorological and tidal data from the Japan Meteorological Agency, to consider different fish species. The study applied feature engineering techniques, incorporating lag features and moving average features. Comparative evaluations were conducted against a baseline model that neither accounts for fish species nor includes lag and moving average features. The proposed method exhibited superior performance across all evaluation metrics compared to the baseline model. Specifically, the proposed method achieved a Root Mean Squared Error (RMSE) of 4.36 compared to the baseline's 5.47, a Mean Absolute Error (MAE) of 3.02 versus 4.16, an R² score of 0.20 compared to -0.27, a Mean Absolute Percentage Error (MAPE) of 74.6% versus 133.0%, and a Median Absolute Error (Median AE) of 2.04 compared to 3.33. These improvements highlight the effectiveness of the proposed model in enhancing predictive accuracy and reliability.

Paper Citation

in Harvard Style

Tanaka T., Tahara Y., Ohsuga A. and Sei Y. (2025). Fish Catch Prediction by Combining Fishing, Weather and Tidal Data. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1108-1115. DOI: 10.5220/0013263200003890

in Bibtex Style

@conference{icaart25,
author={Tomohiro Tanaka and Yasuyuki Tahara and Akihiko Ohsuga and Yuichi Sei},
title={Fish Catch Prediction by Combining Fishing, Weather and Tidal Data},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1108-1115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013263200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Fish Catch Prediction by Combining Fishing, Weather and Tidal Data
SN - 978-989-758-737-5
AU - Tanaka T.
AU - Tahara Y.
AU - Ohsuga A.
AU - Sei Y.
PY - 2025
SP - 1108
EP - 1115
DO - 10.5220/0013263200003890
PB - SciTePress