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

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.