A coupled artificial neural network with artificial rabbits optimizer for predicting water productivity of different designs of solar stills

Abstract

In this study, a coupled multi-layer perceptrons (MLP) model with an artificial rabbits optimizer (ARO) is developed to predict the water productivity of different designs of solar stills (SSs). The investigated SSs are conventional, stepped, pyramid, and tubular SSs. The accuracy of the developed model was compared with three other MLP models optimized with conventional optimizers, namely gradient descent algorithm, particle swarm optimizer (PSO), and genetic algorithm (GA) optimizer. All models were trained and tested using the experimental data of the four SSs under the meteorological conditions of different cities. The performance of all models was evaluated using different statistical measures for all investigated cases. The MLP-ARO outperformed the other three models. The computed root mean square deviation values for all SSs’ designs are 130.79, 57.07, 40.28, and 2.82 ml for MLP, MLP-GA, MLP-PSO, and MLP-ARO models, respectively.