Hybrid Approach to Rainfall Disaggregation Using Artificial Intelligence and Multivariate Disaggregation Considering Spatial Correlation
Akhila, R. ; Pramada, S. K. and Berndtsson, R. LU
(2026)
In Journal of Hydrologic Engineering
31(4).
- Abstract
High-resolution rainfall data are essential for various hydrological applications such as flood modeling, soil erosivity studies, and urban water management. However, the scarcity of subdaily data, particularly in remote regions, necessitates reliable disaggregation methods to derive finer temporal resolutions from daily rainfall records. This study introduces a novel hybrid approach combining the multivariate disaggregation of rainfall (MuDRain) model with artificial neural networks (ANNs) to enhance rainfall disaggregation. MuDRain disaggregates daily rainfall into hourly data while preserving spatial correlations across multiple locations. The methodology involved clustering of 222 grid points into different clusters using k‐means,... (More)
High-resolution rainfall data are essential for various hydrological applications such as flood modeling, soil erosivity studies, and urban water management. However, the scarcity of subdaily data, particularly in remote regions, necessitates reliable disaggregation methods to derive finer temporal resolutions from daily rainfall records. This study introduces a novel hybrid approach combining the multivariate disaggregation of rainfall (MuDRain) model with artificial neural networks (ANNs) to enhance rainfall disaggregation. MuDRain disaggregates daily rainfall into hourly data while preserving spatial correlations across multiple locations. The methodology involved clustering of 222 grid points into different clusters using k‐means, followed by disaggregation for both individual clusters and the entire data set (all stations approach) for Kerala, India. The results were evaluated using descriptive statistics. The “all stations approach” outperformed cluster-specific disaggregation. To further refine the results. A hybrid MuDRain-ANN model is developed further and demonstrates significant advancements in capturing high-intensity events compared to MuDRain alone. Additionally, intensity-duration-frequency (IDF) curves were generated for representative locations, revealing underestimations for shorter durations and better alignment with observed data for longer durations. This comprehensive approach not only enhances the temporal resolution of rainfall data but also provides critical insights for urban drainage design and agricultural planning.
(Less)
- author
- Akhila, R.
; Pramada, S. K.
and Berndtsson, R.
LU
- organization
- publishing date
- 2026-08-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial neural networks (ANNs), Intensity-duration-frequency (IDF), K‐means clustering, Multivariate disaggregation of rainfall (MuDRain), Rainfall disaggregation
- in
- Journal of Hydrologic Engineering
- volume
- 31
- issue
- 4
- article number
- 04026011
- publisher
- American Society of Civil Engineers (ASCE)
- external identifiers
-
- scopus:105036686026
- ISSN
- 1084-0699
- DOI
- 10.1061/JHYEFF.HEENG-6766
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2026 American Society of Civil Engineers.
- id
- 078f02c1-1cc9-4cf8-a0f9-f65565a09400
- date added to LUP
- 2026-05-20 08:45:02
- date last changed
- 2026-05-25 09:41:19
@article{078f02c1-1cc9-4cf8-a0f9-f65565a09400,
abstract = {{<p>High-resolution rainfall data are essential for various hydrological applications such as flood modeling, soil erosivity studies, and urban water management. However, the scarcity of subdaily data, particularly in remote regions, necessitates reliable disaggregation methods to derive finer temporal resolutions from daily rainfall records. This study introduces a novel hybrid approach combining the multivariate disaggregation of rainfall (MuDRain) model with artificial neural networks (ANNs) to enhance rainfall disaggregation. MuDRain disaggregates daily rainfall into hourly data while preserving spatial correlations across multiple locations. The methodology involved clustering of 222 grid points into different clusters using k‐means, followed by disaggregation for both individual clusters and the entire data set (all stations approach) for Kerala, India. The results were evaluated using descriptive statistics. The “all stations approach” outperformed cluster-specific disaggregation. To further refine the results. A hybrid MuDRain-ANN model is developed further and demonstrates significant advancements in capturing high-intensity events compared to MuDRain alone. Additionally, intensity-duration-frequency (IDF) curves were generated for representative locations, revealing underestimations for shorter durations and better alignment with observed data for longer durations. This comprehensive approach not only enhances the temporal resolution of rainfall data but also provides critical insights for urban drainage design and agricultural planning.</p>}},
author = {{Akhila, R. and Pramada, S. K. and Berndtsson, R.}},
issn = {{1084-0699}},
keywords = {{Artificial neural networks (ANNs); Intensity-duration-frequency (IDF); K‐means clustering; Multivariate disaggregation of rainfall (MuDRain); Rainfall disaggregation}},
language = {{eng}},
month = {{08}},
number = {{4}},
publisher = {{American Society of Civil Engineers (ASCE)}},
series = {{Journal of Hydrologic Engineering}},
title = {{Hybrid Approach to Rainfall Disaggregation Using Artificial Intelligence and Multivariate Disaggregation Considering Spatial Correlation}},
url = {{http://dx.doi.org/10.1061/JHYEFF.HEENG-6766}},
doi = {{10.1061/JHYEFF.HEENG-6766}},
volume = {{31}},
year = {{2026}},
}