Improving the Water Quality Classification Model for Various Farms Using Features Based on Artificial Neural Network

Abstract

Measuring and classifying the water quality is necessary to manage the appropriate water quality for various farms near the coast or affected by seawater. This research aimed to improve the water quality classification model for various farms using Multi-Layer Perceptron Neural Network-based multi-class Support Vector Machine. It also implements the Random Forest Feature Importance Selection to increase model accuracy. The class reduction technique decreases the probability of co-occurrence classes for various farms in overlapping water ecosystems. The result has shown that the dataset that applied the class reduction helped increase the model’s efficiency more than the feature selection technique. The models that applied the multi-class Support Vector Machine classifier are more accurate than the Softmax activation function classifier. The findings indicate that the model using Multi-Layer Perceptron Neural Network-based One-versus-One Support Vector Machine combined with the Random Forest Feature Importance Selection and the class reduction has the highest efficiency and improves the water quality classification model in various farms.