Optimized Dissolved Oxygen Prediction Using Genetic Algorithm and Bagging Ensemble Learning for Smart Fish Farm

Abstract

The field of aquaculture is one of the numerous scientific disciplines that benefit greatly from machine learning (ML). The amount of dissolved oxygen (DO), an important indicator of water quality in sustainable fish farming, affects the yield of aquatic production. It is essential to make DO projections in fishing ponds to carry out the process of artificial aeration. We present DO forecasts utilizing time series analysis based on data obtained from Hanwha Aqua Planet Jeju, located in South Korea. This information could form the basis of a data foundation for an early detection system and improved aquaculture farm management. This research presents a unique genetic algorithm (GA) called GA-based XGBoost, CatBoost, and extra tree (GA-XGCBXT) bagging ensemble model based on GAs. This model is built on extreme gradient boosting (XGBoost), CatBoost (CB), and extra trees (XTs). To select the most outstanding features, various methodologies that exhibit a strong association with the primary data were applied. The performance of the proposed model was evaluated by comparing it to actual sensor data that had been observed, both in the training and validation sets. The precise evaluation accuracy of the anticipated results of the recommended GA-XGCBXT model was determined using various performance indices. By utilizing the strategy we suggested, we acquired a root mean square error of 0.310. Our objective is to enhance the ML model for aquaculture so that academics and practitioners can employ applications for smart fish farming with complete reliability.