2018/10/24 by Maitreya Patel, Patel, Maitreya, Anery Patel +4 · 1 citation
Computer Science · Earth and Planetary Sciences · Environmental Science · #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Meteorological Phenomena and Simulations #Neural and Evolutionary Computing (cs.NE) #Time Series Analysis and Forecasting #cs.NE
paper · pdf · doi:10.48550/arxiv.1810.10485
arxiv created 2018/10/24 · openalex publication_date 2018/10/24 · arxiv updated 2018/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Short-term rainfall forecasting, also known as precipitation nowcasting has become a potentially fundamental technology impacting significant real-world applications ranging from flight safety, rainstorm alerts to farm irrigation timings. Since weather forecasting involves identifying the underlying structure in a huge amount of data, deep-learning based precipitation nowcasting has intuitively outperformed the traditional linear extrapolation methods. Our research work intends to utilize the recent advances in deep learning to nowcasting, a multi-variable time series forecasting problem. Specifically, we leverage a bidirectional LSTM (Long Short-Term Memory) neural network architecture which remarkably captures the temporal features and long-term dependencies from historical data. To further our studies, we compare the bidirectional LSTM network with 1D CNN model to prove the capabilities of sequence models over feed-forward neural architectures in forecasting related problems.