Benchmarking Machine Learning Models for Predicting the Average Weight of Oncorhynchus Mykiss in High Andean Fish Farming
DOI:
https://doi.org/10.18687/LACCEI2026.1.1.2262Keywords:
aquaculture, rainbow trout, prediction, machine learning, profitabilityAbstract
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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How to Cite
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