Piran, M. J., Wang, X., Kim, H. J., & Kwon, H. H. (2024). Precipitation Nowcasting Using Transformer-Based Generative Models and Transfer Learning for Improved Disaster Preparedness. International Journal of Applied Earth Observation and Geoinformation, 132, Article 103962.
https://doi.org/10.1016/j.jag.2024.103962

has been cited by the following article:

• TITLE: Precipitation Nowcasting in Dar es Salaam: Comparative Analysis of LSTM and Bidirectional LSTM for Enhancing Early Warning Systems

  AUTHORS: Innocent J. Junior, Jacqueline Benjamin Tukay, Abraham Okrah, Genesis Magara, Daniel J. Masunga

  KEYWORDS: Precipitation Prediction, Long Short-Term Memory, Bidirectional LSTM, Dar es Salaam

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.13 No.4, April 22, 2025

  ABSTRACT: Accurate precipitation forecasting is crucial for mitigating the impacts of extreme weather events and enhancing disaster preparedness. This study evaluates the performance of Long Short-Term Memory and Bidirectional LSTM models in predicting hourly precipitation in Dar es Salaam using a multivariate time-series approach. The dataset consists of temperature, pressure, U-wind, V-wind, and precipitation, preprocessed to handle missing values and normalized to improve model performance. Performance metrics indicate that BiLSTM outperforms LSTM, achieving lower Mean Absolute Error and Root Mean Squared Error by 6.4% and 6.5%, respectively along with improved threshold scores. It demonstrated better overall prediction accuracy. It also improves moderate precipitation detection (TS3.0) by 16.9% compared to LSTM. These results highlight the advantage of bidirectional processing in capturing complex atmospheric patterns, making BiLSTM a more effective approach for precipitation forecasting. The findings contribute to the development of improved deep learning models for early warning systems and climate risk management.