Abstract
This paper investigated the application of Single Layer Feed Forward Neural Network (SLFN), Extreme Learning Machine (ELM) and Regularized Online Sequential - Random Vector Functional Link (ROS-RVFL) neural network for prediction of the Indian Summer Monsoon Rainfall (ISMR) using Sea Surface Temperature (SST), Sea Level Pressure (SLP) and the combination of SST and SLP (SST+SLP) as input predictors. Nine sets of predictions are made using the SLFN, ELM, and ROS-RVFL techniques by applying SST, SLP, and the combination of SST and SLP (SST+SLP) predictors. It is found that when the combination of SST and SLP (SST+SLP) is chosen as the input predictor, the aforesaid neural networks based approaches produce minimal error scores and provide better accuracy. The ROS-RVFL technique outperformed in all the trials used in this study and the ELM technique produced more accurate outcomes than the SLFN technique.
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