Title: Application of cuckoo search in water quality prediction using artificial neural network

Authors: Sankhadeep Chatterjee; Sarbartha Sarkar; Nilanjan Dey; Amira S. Ashour; Soumya Sen; Aboul Ella Hassanien

Addresses: Department of Computer Science and Engineering, University of Calcutta, Kolkata, India ' Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India ' Department of Information Technology, Techno India College of Technology, West Bengal, India ' Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Egypt ' A.K.Choudhury School of Information Technology, University of Calcutta, Kolkata, India ' Information Technology Department, Cairo University, Egypt

Abstract: Domestic and industrial pollution affected the water quality to a greater extent. Recent research studies have achieved reasonable success in predicting the water quality using several machine learning based techniques. In the current work, a proposed cuckoo search (CS) has been applied to improve the support in the classification process during the water quality prediction. The proposed model (NN-CS) gradually minimises an objective function; namely the root mean square error (RMSE) in order to find the optimal weight vector for the artificial neural network (ANN). The proposed model was compared with three other well-established models, namely NN-GA (ANN trained with genetic algorithm) and NN-PSO (ANN trained with particle swarm optimisation) in terms of accuracy, precision, recall, f-measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows index (FM index). The simulation results established superior accuracy of NN-CS over the other models.

Keywords: artificial neural network; ANN; genetic algorithm; particle swarm optimisation; cuckoo search; water quality prediction.

DOI: 10.1504/IJCISTUDIES.2017.089054

International Journal of Computational Intelligence Studies, 2017 Vol.6 No.2/3, pp.229 - 244

Received: 24 Mar 2017
Accepted: 27 Jun 2017
Published online: 04 Jan 2018 *

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