Non-Dominated Sorting Genetic Algorithm-II-Induced Neural-Supported Prediction of Water Quality with Stability Analysis

Sankhadeep Chatterjee (), Sarbartha Sarkar (), Nilanjan Dey () and Soumya Sen ()
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Sankhadeep Chatterjee: Department of Computer Science & Engineering, University of Calcutta, Kolkata, India
Sarbartha Sarkar: #x2020;Department of Mining Engineering, Indian Institute of Technology, (Indian School of Mines), Dhanbad, India
Nilanjan Dey: #x2021;Department of Information Technology, Techno India College of Technology, Kolkata, India
Soumya Sen: #xA7;A. K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India

Journal of Information & Knowledge Management (JIKM), 2018, vol. 17, issue 02, 1-20

Abstract: Water is one of the most important necessities for human survival. In municipal corporation areas, water quality affects a large part of the population. Good quality water supply is an imperative parameter that influences individuals’ health. Automated accurate water quality determination becomes an urgent necessity. Detecting the drinking water quality can prevent such scenarios prior to the critical stage. Recent research works have achieved reasonable success in predicting the water quality by deploying several machine learning-based techniques and utilising different aspects to analyse water quality. The accuracy levels of already proposed models are to be improved, keeping in mind the sensitivity of the problem domain. In the current work, Non-dominated Sorting Genetic Algorithm-II (NN-NSGA-II) was employed to train the artificial neural network (ANN) to improve its performance over its traditional counterparts. The proposed model gradually minimises two different objective functions, namely the root mean square error (RMSE) and Maximum Error (ME) in order to find the optimal weight vector for the ANN. The proposed model was compared with another two well-established models namely ANN trained with Genetic Algorithm (NN-GA) and ANN trained with Particle Swarm Optimisation (NN-PSO) in terms of accuracy, precision, recall, F-Measure, Matthews correlation coefficient (MCC) and Fowlkes–Mallows (FM) index. Furthermore, a data perturbation-based stability analysis is proposed to test the stability of the proposed method. The simulation results established superior accuracy of NN-NSGA-II over the other models.

Keywords: Artificial neural network; genetic algorithm; particle swarm optimisation; non-dominated sorting genetic algorithm-II; water quality prediction (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0219649218500168

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