Weather Prediction by Integrating Recurrent Neural Network Dynamics into Case Based Reasoning

Most of the weather forecasting approaches attempt to forecast only single weather attribute at a time (e.g., temperature, rainfall etc.). If weather attribute(s) is forecasted by Case Based Reasoning (CBR) then similarity between cases is measured by a similarity metric where equal weights or heuristic weights are assigned to all influencing attributes. This paper presents a forecasting method for one day-ahead prediction of multiple weather attributes at a time by case based reasoning (CBR) in local scale, which resolves the attribute weighting problem of CBR using non-linear autoregressive with exogenous inputs neural network (NARXNN) and results a hybrid method for multiple weather attributes forecasting.

Forecasting performance of simple CBR, segmented CBR and hybrid CBR by NARXNN is compared. From the experimental results, superiority of the hybrid method to others is established in forecasting of multiple weather attributes. Collected historical records of weather station from 1980 to 2009 are used for model training, validating and testing.