Adaptive neuro-fuzzy inference system to estimate the predictability of visibility during fog over Delhi, India

Abstract

In the present research it was attempted to estimate the predictability of visibility during fog over the airport of the most polluted city Delhi (28 o 38 0 N, 77 o 12 0 E), India, with an adaptive neuro-fuzzy inference system (ANFIS). The investigation started with the evaluation of fuzzy membership to categorize the data into different ranges. The output variables of fuzzy membership are used as the input in the multilayer perceptron model of artificial neural networks. In this hybrid computing system, the ANFIS was trained with the data from 2000 to 2010 for estimating the predictability of visibility during fog over Delhi airport. The results show that the ANFIS provides minimum forecast errors (9.09%) with 12 hr lead time in comparison to other neural network models and the existing forecast models. The results were validated with observations from 2011 to 2015. The coupled model ANFIS shows minimum error in visibility forecasting during fog over Delhi airport with validation from observations as well. The study therefore suggests that the ANFIS may be adopted as an alternative operational model for forecasting visibility during fog with 90.91% accuracy for a 12 hr lead time.