Authors:
Tomohiro Tanaka
;
Yasuyuki Tahara
;
Akihiko Ohsuga
and
Yuichi Sei
Affiliation:
The University of Electro-Communications, Chofu, Tokyo, Japan
Keyword(s):
Fish Catch Prediction, XGBoost, Meteorological Data, Feature Engineering, Time Series Data.
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 (Med
ian AE) of 2.04 compared to 3.33. These improvements highlight the effectiveness of the proposed model in enhancing predictive accuracy and reliability.
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