Benchmarking Machine Learning Models for Predicting the Average Weight of Oncorhynchus Mykiss in High Andean Fish Farming
DOI:
https://doi.org/10.18687/LACCEI2026.1.1.2262Palabras clave:
aquaculture, rainbow trout, prediction, machine learning, profitabilityResumen
The present research compares Machine Learning models to predict growth (average final weight) and support profitability decisions in a high Andean fish farm. Linear and Random Forest Regression were evaluated using a set of 500 weekly records, integrating historical data provided by the fish farm and synthetic data generated within validated physicochemical ranges. The results concluded with a high performance in the growth prediction (R2>0.98) and adequate performance in profitability (R2≈0.87). In addition, the profitability classifier achieved 0.93 accuracy, suggesting operational utility as an early warning.
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2026-07-27
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Derechos de autor 2026 LACCEI
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Esta obra está bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.
LACCEI conserva el copyright de todos los artículos publicados bajo los términos de su acuerdo de transferencia de copyright. Como titular del copyright, LACCEI distribuye los artículos al público bajo la Licencia Internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0 (CC BY-NC-SA 4.0).
Cómo citar
Uceda Martos, P. J., Sanchez Quiroz, S. J., Chavez Huaman, S. R., Ruiz Regalado, R. A., & Torrel Villanueva, M. E. (2026). Benchmarking Machine Learning Models for Predicting the Average Weight of Oncorhynchus Mykiss in High Andean Fish Farming. LACCEI, 1(14). https://doi.org/10.18687/LACCEI2026.1.1.2262