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

Author

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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)

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.

Suggested Citation

  • Sankhadeep Chatterjee & Sarbartha Sarkar & Nilanjan Dey & Soumya Sen, 2018. "Non-Dominated Sorting Genetic Algorithm-II-Induced Neural-Supported Prediction of Water Quality with Stability Analysis," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.
    • Handle: RePEc:wsi:jikmxx:v:17:y:2018:i:02:n:s0219649218500168
    DOI: 10.1142/S0219649218500168
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