https://github.com/md-hameem/climate-disasters-warning-systems
Climate Disaster Warning System is a deep learning-based project for detecting wildfires, floods, and sea-level rise using satellite and ground data. It leverages ResNet, Vision Transformer (ViT), and GRACE datasets to support early warning systems and climate research.
https://github.com/md-hameem/climate-disasters-warning-systems
climate-science computer-vision deep-learning disaster-detection early-warning-system fire-detection flood-detection geospatial-analysis jupyter-notebook keras netcdf pytorch resnet satellite-data sea-level-rise vision-transformer
Last synced: 4 months ago
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Climate Disaster Warning System is a deep learning-based project for detecting wildfires, floods, and sea-level rise using satellite and ground data. It leverages ResNet, Vision Transformer (ViT), and GRACE datasets to support early warning systems and climate research.
- Host: GitHub
- URL: https://github.com/md-hameem/climate-disasters-warning-systems
- Owner: md-hameem
- License: mit
- Created: 2025-06-21T06:59:03.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-21T07:27:44.000Z (4 months ago)
- Last Synced: 2025-06-21T08:18:17.756Z (4 months ago)
- Topics: climate-science, computer-vision, deep-learning, disaster-detection, early-warning-system, fire-detection, flood-detection, geospatial-analysis, jupyter-notebook, keras, netcdf, pytorch, resnet, satellite-data, sea-level-rise, vision-transformer
- Language: Jupyter Notebook
- Homepage:
- Size: 19.1 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Climate Disaster Warning System
This repository contains the code, models, and supporting materials for detecting and analyzing climate-related disasters—specifically fire, flood, sea-level rise, and earthquake events. The project integrates deep learning and geospatial data analysis to support early warning systems and climate research.
## 📁 Project Structure
```
model/
Fire/
fire_detection_resnet50_V1.h5
Fire_Detection.ipynb
D-Fire/
Flood/
flood_detection.ipynb
optimizer_vit.pth
resnet_confusion_matrix.csv
resnet_hard_predictions.csv
resnet_metrics.pkl
resnet_model_checkpoint.pth
resnet_probability_predictions.csv
resnet_test_metrics_summary.csv
resnet_test_summary_metrics.csv
vit_model.pth
Sea-Level Rise/
CSR_GRACE_GRACE-FO_RL06_Mascons_all-corrections_v02.nc
SLR_GRACE.ipynb
Data/
Earthquake/
input/
test/...
sample_submission.csv
train.csv
earthquake_detection.ipynb
lgbm_flood_4.pkl
lgbm_importances.png
submission.csv
.gitignore
LICENSE
README.md
requirements.txt
```
## 🧠 Models Overview
### 🔥 Fire Detection
* **Model**: ResNet50 (Keras-based)
* **Approach**: Binary image classification (fire vs. non-fire) with transfer learning
* **Justification**: ResNet50's deep architecture and residual connections help mitigate vanishing gradients and boost accuracy on image tasks.
### 🌊 Flood Detection
* **Models**: ResNet and Vision Transformer (ViT)
* **Approach**: Image-based flood classification and evaluation
* **Justification**: ResNet is a proven CNN model, while ViT captures global context via self-attention, enhancing performance in complex flood imagery.
### 🌐 Sea-Level Rise Analysis
* **Data Source**: GRACE satellite NetCDF files
* **Tools**: Data processing and visualization in Jupyter Notebooks
* **Justification**: GRACE data offers precise Earth gravity measurements, enabling accurate inferences about sea-level and mass redistribution trends.
### 🌎 Earthquake Detection
* **Model**: LightGBM Regressor, CatBoostRegressor, SVR, NuSVR, KernelRidge
* **Approach**: Time-series or seismic data analysis for earthquake event detection and prediction
* **Justification**: Deep learning models can capture temporal and spatial patterns in seismic data, improving the accuracy of earthquake detection and early warning.
## 📥 Datasets & Pretrained Models
* **Fire & Flood Datasets**:
Download from:
* [Kaggle Fire Dataset](https://www.kaggle.com/datasets/phylake1337/fire-dataset)
* [Kaggle Flood Dataset](https://www.kaggle.com/datasets/ratthachat/flood-image-dataset)
Place files under:
* `model/Fire/D-Fire/`
* `model/Flood/`
* **Sea-Level Data**:
Download from NASA’s GRACE portal:
[NASA GRACE Data](https://podaac.jpl.nasa.gov/GRACE)
* **Earthquake Data**:
[LANL Earthquake Prediction](https://www.kaggle.com/competitions/LANL-Earthquake-Prediction/data)
Place files under:
* `Earthquake Detection/...`
* **Trained Models**:
Pretrained models can be downloaded from this [Google Drive folder](https://drive.google.com/drive/folders/1uGfHQNVUJ4oqNp0-tL8cfA4jEjHz_Lxc?usp=sharing).
Place them in the appropriate directories as shown in the project structure above.
## 📚 Components
### Fire Detection
* [`Fire_Detection.ipynb`](model/Fire/Fire_Detection.ipynb): Full pipeline for training and evaluating the ResNet50 model.
* `fire_detection_resnet50_V1.h5`: Trained model weights.
* `D-Fire/`: Dataset directory for training/testing.
### Flood Detection
* [`flood_detection.ipynb`](model/Flood/flood_detection.ipynb): Includes training and evaluation of both ResNet and ViT models.
* Evaluation metrics: CSV and PKL files track performance, predictions, and confusion matrices.
### Sea-Level Rise
* [`SLR_GRACE.ipynb`](model/Sea-Level Rise/SLR_GRACE.ipynb): Notebook for visualizing and analyzing NetCDF-formatted satellite data.
* `CSR_GRACE_GRACE-FO_RL06_Mascons_all-corrections_v02.nc`: Satellite data file.
* `Data/`: Additional supporting data.
### Earthquake Detection
* [`earthquake_detection.ipynb`](Earthquake/earthquake_detection.ipynb): Notebook for training and evaluating the earthquake detection model.
## 🚀 Getting Started
1. **Clone the repository**
```bash
git clone https://github.com/md-hameem/Climate-Disasters-Warning-Systems.git
cd Climate-Disasters-Warning-Systems
```
2. **Install Dependencies**
Ensure Python 3.x is installed. Then run:
```bash
pip install -r requirements.txt
```
3. **Run Notebooks**
Launch Jupyter and open the relevant `.ipynb` files in each subdirectory.
## 🗂️ Notes
* Large model files are excluded via `.gitignore`.
* Ensure the appropriate models and datasets are placed in their respective folders before running the notebooks.
## 📄 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
## 📬 Contact
For questions, suggestions, or contributions, feel free to:
* Open an issue or submit a pull request
* Email: