Clustered adaptive neuro-fuzzy inference system models of net radiation flux using temperature series

Abstract

This study proposes three clustered adaptive neuro-fuzzy inference system (ANFIS) models for the prediction of net radiation flux over climatic regions in Nigeria. Five temperature indices data obtained from the Nigerian Meteorological Agency, Lagos covering a period 1983 to 2013 were used as input variables to train and test the ANFIS models. Comparative analyses of the three ANFIS models—grid partitioning clustering (GPC), the subtractive clustering (SC), and the fuzzy c-means clustering (FCM) using the minimum values of coefficient of uncertainty (UII)—showed that ANFIS-GPC performed best in the semi-arid and sub-humid humid regions with values of 0.089 and 0.092, respectively. ANFIS-FCM performed best in the sub-humid dry region with a value of 0.074. ANFIS-SC performed best in the humid region with a value of 0.080. It can be concluded that the proposed models are regional dependent, and they are suitable for the prediction of net radiation flux over respective tropical regions for practical purposes.