Temporal and spatial satellite data augmentation for deep learning-based rainfall nowcasting

Özlem Baydaroğlu, İbrahim Demir

Journal of Hydroinformatics 2024 article cited by 15

importance 6/10 ai assisted Hydrological Forecasting Using AI flood forecasting stage: research

Improving short-term rainfall prediction is needed because climate-change-induced flash floods and severe flooding are increasing.

Flash floods threaten lives and property in flood-prone regions like Türkiye's Marmara and Black Sea areas.

Used a ConvLSTM deep learning model with NASA Giovanni satellite precipitation data, augmented via eight interpolation techniques (spatial, temporal, spatio-temporal), to nowcast rainfall for two flood case studies.

not stated in source

Evidence only supports that the comparative nowcasting study was conducted; no measured outcome is stated.

Venue Journal of Hydroinformatics
Published 2024
Work type article
Authors Özlem Baydaroğlu, İbrahim Demir
Citations 15 (OpenAlex, at import time)
References 93
Model or method ConvLSTM
Organization not stated in source
Primary source https://doi.org/10.2166/hydro.2024.235
Open access diamond
Keywords rainfall nowcasting, ConvLSTM, satellite data, data augmentation, floods
6 / 10

significant — thousands to millions affected

Abstract states climate-change-induced flash floods threaten lives and property in densely populated, flood-prone regions with devastating consequences; nowcasting study on two catastrophic floods.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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