Advancements in Daily Precipitation Forecasting: A Deep Dive into Daily Precipitation Forecasting Hybrid Methods in the Tropical Climate of Thailand

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Publication Details

Author listMuhammad Waqas, Usa Wannasingha Humphries, Phyo Thandar Hliang, Angkool Wangwongchai, Porntip Dechpichai

PublisherElsevier

Publication year2024

Volume number12

Start page102757

ISSN22150161

eISSN2215-0161

URLhttps://methods-x.com/retrieve/pii/S2215016124002103

LanguagesEnglish-Great Britain (EN-GB)


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Abstract

resource management. Precise precipitation forecasting is critical to effective management. This study introduced a Daily Precipitation Forecasting Hybrid (DPFH) technique for central Thailand, which uses three different input-based models to improve prediction accuracy.

• The proposed methods precisely combine the biorthogonal wavelet transformation (BWT) function through BWT-RBFNN (Radial Basis Function Neural Networks) and (BWT-LSTM-RNN)Long Short-Term Memory Recurrent Neural Networks. Comparative analyses reveal that hybrid models perform better than conventional deep LSTM-RNN and Multilayer Perceptron Artificial Neural Networks (MLP-ANN). Although MLP-ANN showed moderate effectiveness, LSTM-RNN displayed notable enhancements, particularly evidenced by an impressive R2 (0.96) in Model M-2.

• The combination of BWT-LSTM-RNN yielded substantial enhancements, constantly surpassing standalone models. Specifically, DPFH-3 exhibited superior performance across multiple observation stations.

• The findings emphasize the efficiency of the BWT-LSTM-RNN models in capturing varied precipitation patterns, highlighting their potential to significantly improve the accuracy of precipitation forecasts, particularly in the context of water resource management in central Thailand.


Keywords

Artificial IntelligenceDeep LearningForecastingNeural networksShort Term Precipitationwavelet Transformation


Last updated on 2024-07-06 at 00:00