Abstract
Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall.
Highlights
Understanding future behaviors of precipitation is important to make plans and adaptation strategies, but the climate system is very complex and normally required sophisticated mathematical models to simulate [1,2]
The four different hybrid models were introduced, and the prediction results were compared with the Artificial Neural Network (ANN), Seasonal Artificial Neural Network (SANN), Autoregressive Integrated Moving Average (ARIMA), and Genetic Algorithm and Simulated Annealing algorithm (GA-SA) models
This study attempted to investigate the applicability of several hybrid models in predicting monthly rainfall at the Ca Mau meteorological station in Vietnam
Summary
Understanding future behaviors of precipitation is important to make plans and adaptation strategies, but the climate system is very complex and normally required sophisticated mathematical models to simulate [1,2]. Modeling the variabilities of rainfall events becomes more challenging when local-scale projections are required. There are numerous methods for rainfall prediction which can be categorized into three groups, including statistical, dynamic and satellite-based methods [3,4]. Statistical methods are, still a standard in rainfall forecasting because of their inexpensive computational demands and time-consuming nature. When a comprehensive understanding of underlying processes is required, the statistical modeling paradigm is favored. There are a number of statistical methods and their applications in environmental studies, in nonlinear hydrological processes [5]. The most traditional statistical method applied in hydrology is Autoregressive Integrated Moving Average (ARIMA) [6,7,8,9].
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