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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References
Luk, K.C., Ball, J.E., Sharma, A.: An application of artificial neural networks for rainfall forecasting. Math. Comput. Model. 33(6–7), 683–693 (2001)
Coulibaly, P., Baldwin, C.K.: Nonstationary hydrological time series forecasting using nonlinear dynamic methods. J. Hydrol. 307(1–4), 164–174 (2005)
Jehanzaib, M., Shah, S.A., Yoo, J., Kim, T.W.: Investigating the impacts of climate change and human activities on hydrological drought using non-stationary approaches. Journal of Hydrology 588, 125052 (2020)
Zhang, X., Mohanty, S.N., Parida, A.K., Pani, S.K., Dong, B., Cheng, X.: Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. IEEE Access 8, 30223–30233 (2020)
Jakkula, V.: Tutorial on Support Vector Machine (SVM) (2006)
Hirani, D., Mishra, N.: A survey on rainfall prediction techniques. Int. J. Comput. Appl. 6(2), 28–42 (2016)
Hasan, N., Nath, N.C., Rasel, R.I.: A support vector regression model for forecasting rainfall (2015)
Granata, F., Gargano, R., de Marinis, G.: Support vector regression for rainfall-runoffmodeling in urban drainage: A comparison with the EPA’s storm water management model. Water (Switzerland) 8(3), 1–13 (2016)
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., Herrnegger, M.: Rainfall – runoff modelling using long short-term memory (LSTM) networks. Hydrol. Earth Syst. Sci. 22(11), 6005–6022 (2018)
Widiasari, I.R., Nugoho, L.E., Widyawan, Efendi, R.: Context-based hydrology time series data for a flood prediction model using LSTM. In: Proceedings - 2018 5th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2018, pp. 385–390 (2018). https://doi.org/10.1109/ICITACEE.2018.8576900
Salehin, I., Talha, I.M., Mehedi Hasan, M., Dip, S.T., Saifuzzaman, M., Moon, N.N.: An Artificial intelligence based rainfall prediction using LSTM and neural network. In: Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020, pp. 5–8 (2020). https://doi.org/10.1109/WIECON-ECE52138.2020.9398022
Poornima, S., Pushpalatha, M.: Prediction of rainfall using intensified LSTM based recurrent neural network with weighted linear units. Atmosphere 10(11) (2019). https://doi.org/10.3390/atmos10110668
Aswin, S., Geetha, P., Vinayakumar, R.: Deep learning models for the prediction of rainfall. In: International Conference on Communication and Signal Processing (2018)
Phillips, P.C.B., Perron, P.: Testing for a unit root in time series regression. Biometrika 75(2), 335 (1988)
Stephens, M.A.: Goodness of fit tests with special reference to tests for exponentiality. U.S. Army Research Office, Department of Statistics, Stanford University, Stanford, California, Technical (22) (1978)
Kim, H.S., Kang, D.S., Kim, J.H.: The BDS statistic and residual test. Stochastic Environ. Res. Risk Assess. 17, 104–115 (2003)
Liu, Z., et al.: Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction. Energy Explor. Exploit. 37(4), 1426–1451 (2019)
Battineni, G., Chintalapudi, N., Amenta, F.: Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). Inform. Med. Unlocked 16(June), 100200 (2019)
Samsudin, R., Shabri, A., Saad, P.: A comparison of time series forecasting using support vector machine and artificial neural network model. J. Appl. Sci. 10(11), 950–958 (2010)
Adam, G., Josh, P.: Deep Learning: A Practitioner’s Approach. O’Reilly (2017)
Rezaie-Balf, M., Kisi, O.: New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine. Hydrol. Res. 49(3), 939–953 (2018)
Kişi, Ö.: Streamflow forecasting using different artificial neural network algorithms. J. Hydrolog. Eng. 12(5), 532–539 (2007)
Salman, A.G., Heryadi, Y., Abdurahman, E., Suparta, W.: Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting. Proc. Comput. Sci. 135, 89–98 (2018)
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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