Enhancing Neural Network Performance for Water Quality Forecasting with Principal Component Analysis in Intensive Aquaculture

This work investigates the application of Principal Component Analysis (PCA) to enhance the performance of a neural network regression model for water quality forecasting in intensive aquaculture. The dataset from an intensive cultivation study includes daily readings of controlled pond biochemical parameters. The standard and PCA-enhanced models had their performance evaluated based on the MSE, MAE, and R². The results demonstrate that the model incorporating PCA outperformed the standard model. The PCA model achieved lower training and testing MSEs, with a notable reduction in MAE. These findings highlight the effectiveness of PCA in improving the accuracy and efficiency of neural network models by reducing dimensionality and emphasizing the most informative features.