EfficientRainNet: Smaller Neural Networks Based on EfficientNetV2 for Rainfall Nowcasting

Abstract

Rainfall nowcasting provides short-term, high-resolution information on the location, intensity, and timing of rainfall, which is crucial for weather forecasting, flood warning, and emergency response. This can help people and organizations make informed decisions to mitigate the impact of severe weather events and reduce the risk of damage and loss of life. There are many attempts at tackling the problem at hand, whether it be numerical models or statistical models that also comprise deep neural networks. Even though nowcast models are quite accurate nowadays and the problem has a saturated literature, current approaches mostly focus on improving the nowcast performance while the computational burden keeps increasing. In this study, we propose EfficientRainNet, which is a convolutional neural network architecture that is based on mobile inverted residual linear bottleneck blocks with a few alterations. We show that EfficientRainNet is able to produce comparable results to those of encoder-decoder convolutional GRUs with only a fraction of the trainable parameters over a radar rainfall dataset for the State of Iowa. Also, for the most part, EfficientRainNet performs better than baselines using persistence- and optical flow-based nowcasting, along with another computational efficiency-focused neural network architecture, Small Attention UNet.

Better Localized Predictions with Out-of-Scope Information and Explainable AI: One-Shot SAR Backscatter Nowcast Framework with Data from Neighboring Region

“…The advancements in hardware and memory, on the other side, remove the major barrier for large data processing. To date, however, most efforts in simulating and synthesizing RS images focus on a few topics, such as forecast or nowcast of weather variables (e.g., precipitation) Wang 2022, Tuyen et al 2022), image super-resolution and downscaling (spatial enhancement) (Sit et al 2023b;2023c;Harris et al 2022), image time series generation (temporal enhancement) (Requena-Mesa et al 2021, Sit et al 2023a, and image translation between different RS sensors (Zhu and Kelly 2021, Czerkawski et al 2022, Vandal et al 2022, whereas synthesizing optical / radar images that capture Earth surface characteristics has not been well studied.…”

Enhancing Hydrological Modeling with Transformers: A Case Study for 24-Hour Streamflow Prediction

“…In addition, streamflow forecasting plays a vital role in numerous aspects of hydrology and water management, including watershed management (Demir and Beck, 2009), agricultural planning (Yildirim and Demir, 2022), flood mapping systems (Li and Demir, 2022), and other mitigation activities (Ahmed et al, 2021;Yaseen et al, 2018). Yet, achieving accurate and reliable predictions poses a challenge due to the inherent complexity of hydrological systems, which include nonlinearity, and unpredictability in the datasets (Honorato et al, 2018;Yaseen et al, 2017, Sit et al, 2023a.…”