Cloudburst prediction

🌟 Inspiration Cloudbursts are sudden, intense rainfall events that can lead to devastating floods, especially in mountainous and urban regions. Inspired by the need for early warning systems and climate resilience, we set out to build a machine learning–based solution to predict cloudbursts before they happen and reduce disaster impact.

💡 What it does Our project predicts the likelihood of a cloudburst event based on historical weather data. It uses machine learning models—Random Forest, Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM)—to classify weather conditions and forecast high-risk scenarios.

🛠️ How we built it Preprocessed and cleaned weather datasets using pandas and NumPy

Applied target encoding and cyclic encoding to features like wind direction

Trained three models:

🌳 Random Forest (baseline)

🧠 ANN

🔁 LSTM (for sequential patterns)

Evaluated models using accuracy, F1-score, precision, recall, and confusion matrices

Finalized the best model and generated output metrics for interpretation

🚧 Challenges we ran into Dealing with imbalanced datasets, since cloudbursts are rare

Training LSTM required significant computation and tuning

Extracting meaningful temporal patterns while avoiding overfitting

Getting clean and relevant weather data

🏆 Accomplishments that we're proud of Successfully implemented multiple ML models with decent accuracy

Achieved an overall 85% prediction accuracy

Built a scalable framework that can be adapted to different regions and datasets

Learned to fine-tune and validate deep learning models effectively

📚 What we learned How to work with time-series and imbalanced data

Hands-on experience with deep learning models like LSTM

How important feature engineering is in improving prediction outcomes

The potential of AI in climate risk mitigation

🔮 What's next for Cloudburst Prediction Integrate real-time weather APIs to make the system live

Use geospatial data and satellite imagery for enhanced accuracy

Deploy the model via a web app or mobile alert system

Collaborate with disaster management authorities for real-world application

Built With

• ann
• jupyter
• keras
• lstm
• matplotlib
• notebook
• numpy
• pandas
• python
• scikit-learn
• seaborn
• tensorflow
• xgboost