# Location Agnostic Adaptive Rain Precipitation Prediction using Deep Learning

arXiv: https://arxiv.org/abs/2402.01208 · HTML: https://mlsanity.com/paper/2402.01208/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [Generalized Source-free Domain Adaptation](https://mlsanity.com/paper/2108.01614/) (2021)
- [Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation](https://mlsanity.com/paper/2401.14422/) (2024)

## Built on by (most cited first, 2 of 2)

- [Wind Power Prediction across Different Locations using Deep Domain Adaptive Learning](https://mlsanity.com/paper/2405.11188/) (2024)
- [Semi-Supervised Deep Domain Adaptation for Predicting Solar Power Across Different Locations](https://mlsanity.com/paper/2508.04165/) (2025)

## Abstract (arXiv)

Rain precipitation prediction is a challenging task as it depends on weather and meteorological features which vary from location to location. As a result, a prediction model that performs well at one location does not perform well at other locations due to the distribution shifts. In addition, due to global warming, the weather patterns are changing very rapidly year by year which creates the possibility of ineffectiveness of those models even at the same location as time passes. In our work, we have proposed an adaptive deep learning-based framework in order to provide a solution to the aforementioned challenges. Our method can generalize the model for the prediction of precipitation for any location where the methods without adaptation fail. Our method has shown 43.51%, 5.09%, and 38.62% improvement after adaptation using a deep neural network for predicting the precipitation of Paris, Los Angeles, and Tokyo, respectively.