Weather prediction by recurrent neural network dynamics

Most of the weather forecasting approaches attempted to forecast only single weather attribute at a time. Moreover, they did not consider generalised weather dynamics hidden in meteorological data. This paper presents a multiple weather attributes forecasting method for one-day-ahead prediction using NARX NN in local scale. In this approach, a case is represented in such a way that it can capture dynamic and chaotic behaviour of weather, case base is segmented to capture seasonality of weather and then NARX NN is used to map nonlinear relationships of the meteorological data as well as the dynamic behaviour of the system over a period of time. Forecasting performance of CBR methods and NARX NN is compared. From the empirical results, superiority of NARX NN method is established in forecasting of multiple weather attributes. For model training, validating and testing, collected historical records of weather station from 1980 to 2009 are used.