# DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data

arXiv: https://arxiv.org/abs/1711.02316 · HTML: https://mlsanity.com/paper/1711.02316/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## Built on by (most cited first, 10 of 10)

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- [Accelerated Solutions of Coupled Phase-Field Problems using Generative Adversarial Networks](https://mlsanity.com/paper/2211.12084/) (2022)
- [Wavelet-SARIMA-Transformer: A Hybrid Model for Rainfall Forecasting](https://mlsanity.com/paper/2509.11903/) (2025)
- [A Spatio-Temporal Deep Learning Approach For High-Resolution Gridded Monsoon Prediction](https://mlsanity.com/paper/2601.02445/) (2026)

## Abstract (arXiv)

Accurate rainfall forecasting is critical because it has a great impact on people's social and economic activities. Recent trends on various literatures show that Deep Learning (Neural Network) is a promising methodology to tackle many challenging tasks. In this study, we introduce a brand-new data-driven precipitation prediction model called DeepRain. This model predicts the amount of rainfall from weather radar data, which is three-dimensional and four-channel data, using convolutional LSTM (ConvLSTM). ConvLSTM is a variant of LSTM (Long Short-Term Memory) containing a convolution operation inside the LSTM cell. For the experiment, we used radar reflectivity data for a two-year period whose input is in a time series format in units of 6 min divided into 15 records. The output is the predicted rainfall information for the input data. Experimental results show that two-stacked ConvLSTM reduced RMSE by 23.0% compared to linear regression.