Abstract
Rainfall forecasting is crucial in hydrology and is one of the challenging tasks in the weather forecasting field. The rainfall data are often full with non-linearity, non-stationarity and non-normality, which makes it very difficult to predict the future values accurately. The aim of this study is to evaluate the capability and applicability of Support Vector Machine (SVM) and Recurrent Neural Network (RNN) models in predicting future values of rainfall using various statistical performances such as MSE, MAE, RMSE and MdAPE. The experimental result revealed that RNN has outperformed SVM model as RNN has the lowest MSE, RMSE and MdAPE values. Despite having the best statistical performance results, RNN unable to capture the movements and directions of the rainfall since the value of prediction not closed to the actual values.
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Acknowledgments
The authors would like to thank the Ministry of Higher Education Malaysia (MOHE) for supporting this research under Fundamental Research Grant Scheme Vot No. FRGS/1/2018/STG06/UTHM/03/3 and partially sponsor by Universiti Tun Hussein Onn Malaysia under Multi-Displinary Grant Vot No. H508.
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Jafri, N.S., Ismail, S., Sadon, A.N., Rahman, N.A., Shaharuddin, S.M. (2022). Support Vector Machine and Recurrent Neural Network Algorithm for Rainfall Forecasting. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_13
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