Grey wolf optimizer and case-based reasoning model for water quality assessment

Citation:
Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, A. E. -karim Mohamed, and O. Hegazy, "Grey wolf optimizer and case-based reasoning model for water quality assessment", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Egypt, Nov. 28-30, 2015.

Date Presented:

Nov. 28-30

Abstract:

This paper presents a bio-inspired optimized classification model for
assessing water quality. As fish gills histopathology is a good biomarker for indicating
water pollution, the proposed classification model uses fish gills microscopic
images in order to asses water pollution and determine water quality.
The proposed model comprises five phases; namely, case representation for
defining case attributes via pre-processing and feature extraction steps, retrieve,
reuse/adapt, revise, and retain phases. Wavelet transform and edge detection algorithms
have been utilized for feature extraction stage. Case-based reasoning
(CBR) has been employed, along with the bio-inspired Gray Wolf Optimization
(GWO) algorithm, for optimizing feature selection and the k case retrieval parameters
in order to asses water pollution. The datasets used for conducted experiments
in this research contain real sample microscopic images for fish gills
exposed to copper and water pH in different histopathlogical stages. Experimental
results showed that the average accuracy achieved by the proposed GWO-CBR
classification model exceeded 97.2% considering variety of water pollutants.

Tourism