Modeling of Precipitable Water Vapor Using an Adaptive Neuro-Fuzzy Inference System in the Absence of the GPS Network

Abstract

his paper constructs an adaptive neuro-fuzzy inference system (ANFIS) model to estimate precipitable water vapor (PWV) in Southeast Asia, particularly in the Peninsular Malaysia, Sabah, and Singapore region. The input to the model is developed using the surface pressure, temperature, and relative humidity. The models are trained and tested using PWV values derived from the global positioning system (GPS). The data used are for May 2012 taken at the Nanyang Technology University of Singapore (NTUS) and Universiti Malaysia Sabah, Kinabalu (UMSK); and for February 2009 taken at the Universiti Kebangsaan Malaysia Bangi (UKMB). The validation process is conducted using June 2012 data for NTUS and UMSK and March 2009 data for UKMB. The performance the ANFIS model is compared with a multilayer perceptron (MLP), Elman neural networks, and multiple linear regression (MLR) models. Results from validations at the three stations showed that the ANFIS model performed well as compared with MLP, Elman neural networks, and MLR, with a mean absolute error of 0.015 mm, a percent error of 0.028%, and root-mean-square error of 0.019 mm. These results suggest that the ANFIS model is a promising approach for estimating PWV values that is cost effective, continuous, and potentially useful for meteorological applications.