Abstract: This literature review analyzes the limitations of deep learning-based weather forecasting models. Models such as LSTM, BiLSTM, GAN-LSTM and FBVS-LSTM currently used are generally trained on single-location and sparse data, which limits their scalability and transferability to other climate domains. Most studies lack transfer learning, multi-location validation and real-time implementation. Long-term forecasting, feature importance analysis and interpretability have also been ignored. Rainfall forecasting still needs improvement, particularly during times of high variability. Furthermore, the majority of models do not make use of contemporary architectures like transformers and are instead based on outdated DL frameworks. . The study also shows that sentiment and situation-aware forecasting cannot be done with single-domain models. Future studies should focus on interpretable, adaptive, and real-time forecasting systems, multi-location data, and contemporary DL methodologies.
Keywords: LSTM, CNN, GA, RNN, NWP, ANN.
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DOI:
10.17148/IJARCCE.2026.15208
[1] Jitendra Kumar Saini, Varun Bansal, Vishal Kumar, Arun Saini, "Improving Weather Forecasting Precision Using LSTM-Based Deep Learning Models," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15208
