Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall

One of the most challenging tasks in rainfall prediction is designing a reliable computational methodology owing the random and stochastic characteristics of time-series. In this study, the potential of five different data-driven models including Multilayer Perceptron (MLP), Least Square Support Vector Machine (LSSVM), Neuro-fuzzy, Hammerstein-Weiner (HW) and Autoregressive Integrated Moving Average (ARIMA) were employed for multi-station (Hien, Thank My, Hoi Khanh, Ai Nghia and Cai Lau) prediction of daily rainfall in the Vu Gia-Thu Bon River basin in Central Vietnam. Subsequently, hybrid ARIMA-MLP, ARIMA-LSSVM, ARIMA-NF and ARIMA-HW models were also utilized to predict the daily rainfall at these stations. The results were evaluated in terms of widely used performance criteria, viz.: determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC). Besides, the Taylor diagram is also used to examine and compare the similarity between the observed and predicted rainfall. The quantitative analysis indicated that the HW model increased the prediction accuracy by 5%, 3% and 2% at Hien, Ai Nghia and Cau Lau stations, respectively, compared to the other models. Likewise, the NF model increased the prediction accuracy at Thanh My and Hoi Khanh stations in contrast to the other models in terms of the mean absolute error. Also, the results of hybrid ARIMA-NF and ARIMA-HW models showed the best performance in terms of predictive skills and verified to increase the prediction accuracy in comparison to the single models.

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