Comparative Assessment of ML Models for Water Quality Prediction in Biofloc Aquaculture: A Data-Driven Approach

During the cutting edge technology of Machine Learning (ML) and Deep Learning (DL), plays a vital role for finding the best approach for data pre-processing, model selection, training and testing, prediction and analysis, result analysis, accuracy of predictions. Using the methodologies of ML models the real-time monitoring of biofloc model can be achieved through which the water quality can be predicted. Different researches have been carried out with multiple ML models on biofloc water quality dataset but through this article the genuine dataset provided by central government is experimented with ML and DL. Finally with different parameters the models have been tested and results have been displayed.