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Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks

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  • Duong Tran Anh

    (Institute of Hydraulic and Water Resources Engineering, Technical University of Munich, Arcisstrasse 21, D-80333 Munich, Germany
    Environment, Water and Climate Change Adaptation research group, Vietnamese-German University, Le Lai Street, Hoa Phu Ward, 820000 Thu Dau Mot City, Binh Duong Province, Vietnam)

  • Thanh Duc Dang

    (Engineering Systems and Deign Pillar, Singapore University of Technology and Design, 8 Somapah Road, Tampines 487372, Singapore
    Environment, Water and Climate Change Adaptation research group, Vietnamese-German University, Le Lai Street, Hoa Phu Ward, 820000 Thu Dau Mot City, Binh Duong Province, Vietnam)

  • Song Pham Van

    (Environment, Water and Climate Change Adaptation research group, Vietnamese-German University, Le Lai Street, Hoa Phu Ward, 820000 Thu Dau Mot City, Binh Duong Province, Vietnam)

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.

Suggested Citation

  • Duong Tran Anh & Thanh Duc Dang & Song Pham Van, 2019. "Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks," J, MDPI, vol. 2(1), pages 1-19, February.
    • Handle: RePEc:gam:jjopen:v:2:y:2019:i:1:p:6-83:d:205955
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    References listed on IDEAS

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