Revolutionizing Rain Prediction: Deep Learning-Powered TensorFlow Solution for Meteorology and Emergency Management
In one sentence. CNN-LSTM model predicts rainfall from meteorological variables and is claimed to beat statistical methods, though the abstract gives no quantitative results.
Abstract
Meteorology and emergency management need rain prediction. A brilliant deep learning and TensorFlow project solves this problem. Our solution first uses a Convolutional Neural Network (CNN) to understand complicated spatial patterns in the input data, then an LSTM network to record complex temporal correlations between meteorological variables and precipitation. To meet the challenge, we fine-tuned our model using vast climate data. We assessed our effort using MAE and RMSE on a separate test set. The CNN-LSTM model surpasses statistical methods, enabling accurate precipitation predictions. Our innovation impacts many. Real-time rainfall prediction helps disaster management and agricultural planning. We do more. Our model may add meteorological parameters. This innovation improves weather forecasting beyond precipitation. Our work shows how TensorFlow, state-of-the-art deep learning, and creativity can function together. By successfully anticipating rainfall, we are advancing weather forecasting and disaster management.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- CNN / U-Net, RNN / LSTM / ConvLSTM
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- OpenAlex:W7218291290
- Added
- 2026-10-22
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