Updated README.md

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README.md

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-## 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`
-
----
-
-### 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.
+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+metrics:
+- r_squared
+library_name: keras
+tags:
+- Climate
+- SDL-radiation
+- CNN
+- BiLSTM
+- Time-Series
+---
+
+## 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`
+
+---
+
+### 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.