Maximizing daily rainfall prediction accuracy with maximum overlap discrete wavelet transform-based machine learning models

doi:10.1002/joc.8530

Maximizing daily rainfall prediction accuracy with maximum overlap discrete wavelet transform-based machine learning models

2024-06-12

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DOI：10.1002/joc.8530

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Rainfall is an important phenomenon for various aspects of human life and the environment. Accurate prediction of rainfall is crucial for a wide range of sectors, including agriculture, water resources management, energy production, disaster management and many more. The ability to predict rainfall in an accurate fashion enables stakeholders to make informed decisions and take necessary actions to mitigate the impacts of natural disasters, water scarcity and other issues related to rainfall. In addition, advances in rainfall prediction technologies have the potential to contribute to sustainable water management and the preservation of water resources by providing the necessary information for decision-makers to plan and implement effective water management strategies. Hence, it is important to continuously improve the accuracy of rainfall prediction. In this paper, the integration of the maximum overlap discrete wavelet transform (MODWT) and machine learning algorithms for daily rainfall prediction is proposed. The main objective of this study is to investigate the potential of combining MODWT with various machine-learning algorithms to increase the accuracy of rainfall prediction and extend the forecast time horizon to 3 days. In addition, the performances of the proposed hybrid models are contrasted with the models hybridized with commonly used discrete wavelet transform (DWT) algorithms in the literature. For this, daily rainfall raw data from three rainfall observation stations located in Turkey are used. The results show that the proposed hybrid MODWT models can effectively improve the accuracy of precipitation forecasting, based on model evaluation measures such as mean square error (MSE) and Nash-Sutcliffe coefficient of efficiency (CE). Accordingly, it can be concluded that the integration of MODWT and machine learning algorithms have the potential to revolutionize the field of daily rainfall prediction. This study explores the utilization of maximum overlap fractional wavelet transform (MODWT)-based machine learning models to enhance the precision of daily precipitation predictions. The primary objective of this research is to leverage cutting-edge forecasting techniques to analyse meteorological data. In this context, we combine the power of the maximum overlap fractional wavelet transform with machine learning algorithms to enhance the accuracy of rainfall forecasts. Furthermore, we compare the results of this novel approach with the conventional discrete wavelet decomposition method often encountered in the literature. The research findings demonstrate that integrating machine learning models with the MODWT method holds substantial promise for future weather predictions, consistently achieving outstanding results. image

Keyword:

hybrid

machine learning

maximum overlap discrete wavelet transform

prediction

preprocessing

rainfall

wavelet

Maximizing daily rainfall prediction accuracy with maximum overlap discrete wavelet transform-based machine learning models

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