README.md

---
license: cc-by-nc-sa-4.0
language:
- en
metrics:
- r_squared
library_name: keras
tags:
- Climate
- SDL-radiation
- CNN
- BiLSTM
- Time-Series
model-index:
  - name: SDL Prediction Model
    results:
      - task:
          type: spatio-temporal-forecasting
        dataset:
          type: weather
          name: CLARA-dataset
        metrics:
          - name: R2
            type: r2
            value: 0.8204
---

## SDL Prediction Model

This repository contains a deep learning model for forecasting **Surface Downward Longwave Radiation (SDL)** using a hybrid **CNN-BiLSTM** architecture. The model is trained on the CLARA dataset and is designed for monthly grid-based predictions over India.

---

### Model Overview

- **Model Type:** Convolutional Neural Network + Bidirectional LSTM (CNN-BiLSTM)
- **Version:** 1.0 (`r2-0.8204`)
- **Input Shape:** 12 timesteps × 140 × 140 grid × 4 channels
- **Channels:**
	- `sdlr_value`
	- `year_channel`
	- `month_sin_channel`
	- `month_cos_channel`
- **Model File:** `model.keras`
- **Scalers:**
	- Input: `scalers/input_scalers.pkl`
	- Output: `scalers/y_scaler.pkl`
- **Metadata:**
	- Initial sequence: `metadata/initial_X_sequence_for_forecast.npy`
	- Model metadata: `metadata/model_metadata.json`
- **Model Metrics**
    - `MSE: 0.0099`
	- `MAE: 0.0744`
	- `RMSE: 0.0995`
	- `R2: 0.8204`
	- `MAE (Original SDL Units): 27.88 W/m²`
	- `RMSE (Original SDL Units): 37.26 W/m²`
---

### Dataset

- **Name:** CLARA-dataset
- **Description:** SDL - Surface downward longwave radiation
- **Temporal Resolution:** Monthly
- **Spatial Resolution:** 0.25° × 0.25° grid (140 × 140)
- **Temporal Coverage:** 
    - Start: 1979-01-01
    - End: 2025-06-01
- **Geographic Coverage:**
	- Latitude: 5.0°N to 40.0°N
	- Longitude: 65.0°E to 100.0°E

---

### Repository Structure

```
├── config.json                # Model and dataset configuration
├── LICENSE                    # License (CC BY-NC-SA 4.0)
├── model.keras                # Trained model 
├── metadata/
│   ├── initial_X_sequence_for_forecast.npy
│   └── model_metadata.json
├── scalers/
│   ├── input_scalers.pkl
│   └── y_scaler.pkl
```

---

### Usage


This repository provides the trained model and all necessary artifacts (scalers, metadata, initial input sequence) for SDL prediction, but does **not** include the inference code.

To use the model for inference:

1. **Load the Model and Artifacts:**
	- Load `model.keras` using Keras: `tf.keras.models.load_model('model.keras')`.
	- Load the scalers (`scalers/input_scalers.pkl`, `scalers/y_scaler.pkl`) and metadata (`metadata/model_metadata.json`) using Python's `pickle` and `json` modules.
	- Load the initial input sequence (`metadata/initial_X_sequence_for_forecast.npy`) using NumPy.

2. **Prepare Input Data:**
	- Prepare a 12-month sequence of input data as a 4D array: `(TIMESTEPS, height, width, n_channels)`.
	- Use the provided scalers to normalize input features.

3. **Perform Prediction:**
	- Pass the input sequence to the model to obtain predictions for future SDL values.
	- For rolling forecasts, update the input sequence with each new prediction and repeat as needed.
	- Use the output scaler to inverse-transform predictions to original units (W/m²).

4. **Postprocess Results:**
	- Map predictions to the desired grid points using the latitude and longitude grids from the metadata.

**Note:**
- An implementation of this model is used in the project [Solarwise](https://github.com/RakshitRabugotra/solarwise) repository.

---

### License

This project is licensed under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)**. See the `LICENSE` file for details or visit this [website](https://creativecommons.org/licenses/by-nc-sa/4.0/).

---

### Citation

If you use this model or code, please cite appropriately and respect the license terms.

---

### Contact

For questions or collaboration, please open an issue or contact the repository maintainer.