Data Standardization Analysis for Water Quality Parameters of Nursery Aquaculture

Nursery Aquaculture is necessary for the growth of aquatic animals. A healthy breeder or marine animal must be a relevant environmental factor for aquaculture. It consists of temperature, acid-base, total dissolved solids, dissolved oxygen, and total alkalinity, which are the main factors, and there are other factors. In this paper, we collected data from experiments on inappropriate environmental factors with a test kit measuring twice a day at 10:00 AM and 5:00 PM for 64 days. Then we checked the data by the boxplot method to see a data abnormality. Subsequently, we performed principal component analysis to reduce unimportant water quality parameters and applied algorithms to analyze water quality monitoring data and determine the quality of the water samples. Finally, we found that random forest was the most suitable algorithm for standardizing the data with precision 86%, which we will apply to more complex data matrices.