Uncertainty Evaluation of Deep Learning Models Using an Artificial Rainfall

Accurate rainfall prediction is essential not only for water resource management but also for forecasting and mitigating the impacts of climate change-driven weather events such as floods and droughts. Due to the high spatiotemporal variability of complex meteorological phenomena like rainfall, effective prediction necessitates in high-quality data collection, model application, and uncertainty analysis. Unlike existing studies that focus primarily on developing deep learning models to improve rainfall prediction accuracy, this study evaluates the uncertainty of rainfall predictions using pre-existing deep learning models, U-Net and ConvLSTM, with artificially generated elliptical rainfall data. Artificial rainfall data were designed with four temporal patterns: constant, gradually increasing, gradually decreasing, and time-varying. These patterns were applied in horizontal, vertical, and diagonal movements to evaluate the models' ability to handle spatiotemporal complexity. The results indicate that both deep learning models exhibited spatial smoothing issues on rainfall predictions over time. However, the U-Net model demonstrated superior spatiotemporal performance compared to ConvLSTM. While this study focuses solely on deep learning models for rainfall prediction, future research will consider factors such as data complexity and loss functions to conduct a comprehensive evaluation of prediction uncertainty. This work is expected to contribute to the development of methodologies for rainfall modeling using deep learning approaches.

 

Funding: This research was supported by Disaster-Safety Platform Technology Development Program of the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT. (No. 2022M3D7A1090338)