Water quality classification approach based on bio-inspired Gray Wolf Optimization

This paper presents a bio-inspired optimized classification approach for assessing water quality. As fish liver histopathology is a good biomarker for detecting water pollution, the proposed classification approach uses fish liver microscopic images in order to detect water pollution and determine water quality. The proposed approach includes three phases; preprocessing, feature extraction, and classification phases. Color histogram and Gabor wavelet transform have been utilized for feature extraction phase. The Machine Learning (ML) Support Vector Machines (SVMs) classification algorithm has been employed, along with the bio-inspired Gray Wolf Optimization (GWO) algorithm for optimizing SVMs parameters, in order to classify water pollution degree. Experimental results showed that the average accuracy achieved by the proposed GWO-SVMs classification approach exceeded 95% considering a variety of water pollutants.