Abstract
This paper proposes an ensemble method based on neural network architecture and stacking generalization. The objective is to develop a novel ensemble of Artificial Neural Network models with back propagation network and dynamic Recurrent Neural Network to improve prediction accuracy. Historical meteorological parameters and rainfall intensity have been used for predicting the rainfall intensity forecast. Hourly predicted rainfall intensity forecast are compared and analyzed for all models. The result shows that for 1 h of prediction, the neural network ensemble forecast model returns 94% of precision value. The study achieves that the ensemble neural network model shows significant improvement in prediction performance as compared to the individual neural network model.
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References
Tyagi V, Mishra A (2014) A survey on ensemble combination schemes of neural network. Int J Comput Appl 95(16):18–21
Haidar A, Verma B, Sinha T (2018) A novel approach for optimizing ensemble components in rainfall prediction. In: 2018 Proceeding of IEEE congress on evolutionary computation (CEC), no 978, pp 1–8
Goss DFE (1993) Forecasting with neural networks: an application using bankruptcy data. Inf Manag 24(3):159–167
Kashiwao T, Nakayama K, Ando S, Ikeda K, Lee M, Bahadori A (2017) A neural network-based local rainfall prediction system using meteorological data on the Internet: a case study using data from the Japan Meteorological Agency. Appl Soft Comput 56:317–330
Klent Gomez Abistado CNA, Maravillas EA (2014) Weather forecasting using artificial neural network and Bayesian network. J Adv Comput Intell Intell Inf. 18(5):812–817
Lawrence S, Giles CL, Tsoi AC (1996) What size neural network gives optimal generalization ? Convergence properties of backpropagation, Networks, no. UMIACS-TR-96-22 and CS-TR-3617, pp 1–37
Kanigoro B, Salman AG (2016) Recurrent gradient descent adaptive learning rate and momentum neural network for rainfall forecasting. In: 2016 international seminar on application for technology of information and communication (ISemantic), pp 23–26
Mohd R, Butt MA, Zaman Baba M (2019) SALM-NARX: Self adaptive LM-based NARX model for the prediction of rainfall. In: Proceedings of International Conference on I-SMAC (IoT Soc. Mobile, Anal. Cloud), I-SMAC 2018, pp 580–585
Noor HM, Ndzi D, Yang G, Safar NZM (2017) Rainfall-based river flow prediction using NARX in Malaysia. In: Proceedings of 2017 IEEE 13th international colloquium on signal processing and its applications, CSPA 2017
Razak IAWA, Abidin IZ, Siah YK, Abidin AAZ, Rahman TKA (2017) Ensemble of ANN and ANFIS for water quality prediction and analysis—a data driven approach. J Telecommun Electron Comput Eng 9(2–9):117–122
Ma Z, Wang P, Gao Z, Wang R, Khalighi K (2018) Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose. PLoS ONE 13(10):1–12
Velasco LCP, Granados ARB, Ortega JMA, Pagtalunan KVD (2018) Performance analysis of artificial neural networks training algorithms and transfer functions for medium-term water consumption forecasting. Int J Adv Comput Sci Appl (IJACSA) 9(4):109–116
Souto YM, Porto F, Moura AM, Bezerra EA (2018) A Spatiotemporal ensemble approach to rainfall forecasting. In: Proceedings of international joint conference on neural networks, July 2018, pp 1–8
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This work is sponsored by University Tun Hussein Onn Malaysia.
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Mohd Safar, N.Z., Ndzi, D., Mahdin, H., Khalif, K.M.N.K. (2020). Rainfall Intensity Forecast Using Ensemble Artificial Neural Network and Data Fusion for Tropical Climate. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_24
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DOI: https://doi.org/10.1007/978-3-030-36056-6_24
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