Traditional rainfall prediction models are typically

The gap

Evidence profile

Sourced from the stated research gap and future-work section of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 9 times in total.

Supporting evidence — 3 representative gaps

• Accurate Rainfall Prediction Using GNSS PWV Based on Pre-Trained Transformer Model (2025) · Remote Sensing · cited 9× · doi

  Traditional rainfall prediction models are typically trained on datasets specific to one region, limiting their generalization ability. The scarcity of data in certain areas hinders the development of accurate rainfall prediction models. Previous studies mainly focus on rainfall occurrences, while hourly rainfall prediction remains a challenge.

  generalstated research gap
  Keywords: traditional rainfall prediction models typically trained datasets specific
• Modeling air temperature from snow-buried sensors to refine multi-decadal warming trends in Great Basin National Park, NV, USA (2026) · Theoretical and Applied Climatology · doi

  Future studies could explore the use of other machine learning algorithms to predict daily temperatures from snow-covered sensors. Future studies could evaluate the performance of the model in other mountain environments. Future studies could investigate the use of other datasets, such as satellite data, to provide regional context for the model-adjusted Lascar temperature trends.

  generalfuture-work sectionevidence 5/5
  Keywords: future studies explore use other machine learning algorithms
• Hybrid Deep Learning Architectures for Multi-Horizon Precipitation Forecasting in Mountainous Regions: Systematic Comparison of Component-Combination Models in the Colombian Andes (2026) · Hydrology · doi

  The paper identifies a gap in the ability of conventional deep learning approaches to capture the complex spatial relationships in mountainous terrain. The paper highlights the need for data-driven approaches that can leverage satellite-derived precipitation estimates and digital elevation models. The paper notes that traditional numerical weather prediction models struggle to capture localized precipitation patterns in the Colombian Andes.

  generalstated research gapevidence 5/5
  Keywords: paper identifies gap ability conventional deep learning approaches

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