paper
2019
Downscaling & super-resolution
Climate analysis & attribution Application study
Classical ML (trees, GP, linear, kernels)OtherClimate projections / centennial Journal
2019-07-26
008
Jul 2019
K. Vinoth Kumar, V. P. Singh, Thendiyath Roshni
Journal of Water and Climate Change
Feed-forward and wavelet neural networks statistically downscale GCM predictors to Bagmati basin precipitation, projecting 2014–2036 decreases at the study sites.
Downscaling & super-resolution
Efficacy of hybrid neural networks in statistical downscaling of precipitation of the Bagmati River basin
In one sentence. Feed-forward and wavelet neural networks statistically downscale GCM predictors to Bagmati basin precipitation, projecting 2014–2036 decreases at the study sites.
Labels
- Topic
- Downscaling & super-resolution, Climate analysis & attribution
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels), Other
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.2166/wcc.2019.259 OpenAlex:W2966345212
- Added
- 2026-10-22
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