Architectural optimization of a multilayer perceptron (MLP) neural network enhanced by the Levenberg–Marquardt algorithm for predicting relative humidity: application to Tangier, Morocco

Abstract

This study aimed at establishing a powerful model, using multilayer perceptron (MLP) artificial neural networks, while optimizing database distribution, hidden layer quantity, and node number with the Levenberg–Marquardt learning algorithm, to predict relative humidity in Tangier city, Morocco. The study used a meteorological database with daily readings of 7 variables to predict the output of relative humidity, recorded between January 1985 and December 2022 (13 869 days). The efficacy of the developed models was evaluated by contrasting performance metrics, including coefficient of correlation and mean square error. The best MLP model for the prediction of relative humidity in Tangier has a [7-13-1] architecture, where the hidden layer uses the 'Tansig' function and the output layer uses the 'Purelin' function. The optimized model's efficiency is demonstrated by performance metrics: R = 0.983 and MSE = 0.00295. This MLP model is more efficient than the models established by multiple linear regression, S-MLR and MLR (R ≈ 0.930;  MSE ≈ 10.12). These results have practical applications for better understanding and adapting to climate variations in this region.