Abstract
Disaggregating rainfall data is a crucial step in producing accurate hydrological and meteorological assessments because it refines coarse-resolution rainfall data into finer temporal or spatial scales. To generate high-resolution rainfall data, the Microcanonical Multiplicative Random Cascade (MMRC) model is a stochastic method used in rainfall disaggregation. The MMRC model is a reliable process for producing high-resolution rainfall data. However, it tends to overstate extreme precipitation events. This is because MMRC assumes a predefined method of classification which does not always represent the behaviour of extreme events. To address overestimation in the basic MMRC model, it was modified into MMRC with K-means clustering (MMRC-K), resulting in improved outcomes. Seeking additional enhancements, DBSCAN was integrated into the MMRC model to refine classification and further improve accuracy, especially in preserving extreme precipitation characteristics. An unsupervised machine learning approach called DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to categorize data points based on their density and spatial proximity. By automatically calculating the number of clusters based on the data and successfully recognizing and controlling outliers, DBSCAN can improve outcomes. In this study, we used DBSCAN for data categorization and combined it with the MMRC model for rainfall disaggregation to successfully preserve the characteristics of extreme events. The results showed a considerable improvement in the intensity–duration–frequency (IDF) curve, effectively protecting the representation of extreme rainfall. This approach can be an efficient approach for disaggregating daily rainfall into an hourly scale when high-resolution data is unavailable. MMRC-DBSCAN can be used in a variety of hydrological and meteorological applications, such as flood forecasting, water resource management, and climate change studies.
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Acknowledgements
We would like to thank IIEST Shibpur for providing the facilities and infrastructure to complete this study. We would also like to thank India Meteorological Department for providing the required data to carry out the project.
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D.P.C.: Methodology, Software, Conceptualization, Writing—original draft.
U.S.: Supervision, Writing—review &; editing.
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Chowdhury, D.P., Saha, U. Improvement of extreme rainfall characteristics for disaggregation of rainfall using MMRC with machine learning based DBSCAN clustering algorithm. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01309-3
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DOI: https://doi.org/10.1007/s12145-024-01309-3