A Robust Framework for Dissolved Oxygen Forecasting in Precision Aquaculture: A LightGBM Approach with Advanced Feature Engineering

Nyoman Wira Prasetya; Richard Wijaya Harianto

doi:10.18860/mat.v18i1.37617

A Robust Framework for Dissolved Oxygen Forecasting in Precision Aquaculture: A LightGBM Approach with Advanced Feature Engineering

Nyoman Wira Prasetya, Richard Wijaya Harianto

Abstract

Accurate prediction of necessary water quality parameters such as Dissolved Oxygen (DO) is very critical in precision aquaculture and is essential for performance-based decision-making. This thesis fills the gap between reactive monitoring and predictive intelligence through the construction of a solid machine learning infrastructure. We convert high frequency multivariate time series data into a supervised learning problem by an advanced feature engineering process that generates temporal predictions including lag features and rolling window statistics. A Light Gradient Boosting machine (LightGBM) algorithm trained using the above-mentioned engineered dataset has an extreme predictive power. Results of single-variable interpretation analysis showed that short term data, especially the 5-minute rolling statistics of DO and turbidity variability, are the main driving factors for the model prediction. This research confirms that a feature-engineered LightGBM approach is a computationally efficient, but highly accurate approach to supporting the development of early warning systems in modern aquaculture as a computationally scalable approach.

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DOI: https://doi.org/10.18860/mat.v18i1.37617

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