Pakistan faces severe flooding challenges during July-August monsoon periods, with recent events highlighting the critical need for predictive climate modeling. This project develops a production-ready machine learning system analyzing 116 years (1901-2016) of Pakistan's national climate data to predict rainfall and temperature patterns. The system employs a hybrid modeling architecture with advanced feature engineering for temperature prediction and an optimized simple approach for rainfall forecasting.
Key Achievement: The system achieves R² = 0.992 for temperature prediction and R² = 0.716 for rainfall prediction using Pakistan's average climate data, providing reliable forecasting capabilities for flood risk assessment during critical monsoon periods.
Pakistan experiences devastating floods during July-August monsoon seasons, causing significant economic and humanitarian impacts. This predictive system addresses the urgent need for accurate climate forecasting to support:
- Early warning systems for flood-prone regions
- Agricultural planning and crop management
- Water resource management
- Emergency preparedness protocols
The analysis utilizes comprehensive national average climate data for Pakistan, providing country-level insights while acknowledging regional variations. The 116-year historical dataset enables robust pattern recognition and long-term trend analysis essential for understanding Pakistan's complex monsoon-driven climate system.
The system implements a sophisticated dual-approach architecture:
Temperature Prediction: Advanced feature engineering with 67 engineered features achieving exceptional accuracy (R² = 0.992) Rainfall Prediction: Optimized simple feature approach ensuring robust performance (R² = 0.716) after comprehensive experimentation
This hybrid strategy was developed through iterative experimentation, where initial complex approaches for rainfall modeling yielded suboptimal results, leading to the successful fallback strategy while maintaining advanced methodologies for temperature prediction.
The system employs a comprehensive 67-feature engineering pipeline with an 81.48% safety ratio for data leakage prevention:
Temporal Features: Year normalization, cyclical month encoding, seasonal decomposition Lag Features: Multi-horizon historical values (1, 2, 3, 6, 12 months) Rolling Statistics: Adaptive window moving averages and volatility measures Meteorological Features: Rainfall-temperature interactions, anomaly detection, extreme event indicators Seasonal Intelligence: Pakistan-specific monsoon patterns, winter precipitation cycles
The feature selection process implements ensemble-based validation with multiple cross-validation layers to ensure production stability and prevent overfitting in the Pakistan-specific climate context.
Phase 1: Advanced feature engineering applied to both rainfall and temperature targets Phase 2: Advanced algorithms with complex features for rainfall modeling (yielded suboptimal results) Phase 3: Strategic fallback to optimized simple approach for rainfall while maintaining advanced features for temperature
This adaptive methodology demonstrates systematic model development with empirical validation at each stage.
- Linear Regression (baseline performance benchmarking)
- Ridge Regression (L2 regularization for stability)
- Random Forest (ensemble robustness)
- Gradient Boosting (sequential learning optimization)
- XGBoost (gradient boosting with advanced regularization)
- Support Vector Regression (kernel-based non-linear modeling)
- Temperature Model: R² = 0.992, demonstrating exceptional predictive accuracy
- Rainfall Model: R² = 0.716, providing reliable forecasting for flood risk assessment
- Cross-validation: Robust performance across multiple validation folds
- Hyperparameter Optimization: GridSearchCV with systematic parameter tuning
The system incorporates specific analysis of Pakistan's July-August flood risk periods, utilizing:
- Coefficient of variation analysis for extreme event detection
- Monsoon intensity pattern recognition
- Historical flood correlation analysis
- Seasonal vulnerability assessment
Pearson Correlation: 0.2031 between rainfall and temperature Statistical Significance: Significant but weak correlation reflecting Pakistan's complex meteorological dynamics Interpretation: Independent climate processes requiring specialized modeling approaches for each target variable
Comprehensive visualization suite covering:
- 116-year climate trend analysis
- Seasonal pattern decomposition
- Extreme weather event identification
- Regional climate variability assessment
- Cross-validation performance metrics
- Residual analysis and error distribution
- Feature importance ranking
- Prediction accuracy across different seasons
- Real-time prediction interface
- Historical pattern exploration
- Flood risk assessment tools
- Seasonal forecasting capabilities
Interactive web application providing:
- Real-time climate predictions
- Historical data exploration
- Flood risk assessment interface
- Model performance monitoring
RESTful API service offering:
- Programmatic prediction endpoints
- Batch processing capabilities
- Model metadata access
- Production-grade error handling
- joblib format: Optimized for sklearn-based models
- pickle format: Cross-platform compatibility
- Metadata storage: Model performance and configuration details
- Inference functions: Standalone prediction capabilities
pip install pandas numpy scikit-learn xgboost lightgbm
pip install matplotlib seaborn plotly streamlit flask joblibimport joblib
# Load production inference function
predict_climate = joblib.load('inference_function.joblib')
# Generate prediction for flood-critical period
prediction = predict_climate(year=2024, month='august')
print(f"August Rainfall Forecast: {prediction['rainfall']:.2f} mm")
print(f"August Temperature Forecast: {prediction['temperature']:.2f}°C")For Streamlit App, it's better to directly run it via Google Colab Notebook
For Flask, download the flask_app folder, create a Virtual Environmennt, install the requirements.txt, and run the below command:
python app.py- Root Mean Square Error (RMSE): Quantitative accuracy assessment
- Mean Absolute Error (MAE): Absolute prediction deviation
- R-squared (R²): Variance explanation capability
- Cross-validation scores: Generalization performance validation
- Time-series cross-validation: Temporal data integrity preservation
- Seasonal stratification: Performance consistency across Pakistan's climate seasons
- Holdout testing: Final model validation on unseen data
- Statistical significance testing: Confidence interval analysis
- Temporal Range: Historical data limited to 1901-2016 period
- Spatial Resolution: National average data may not capture regional variations
- Climate Change: Recent climate shifts may not be fully represented
- Feature Dependency: Prediction accuracy depends on input feature availability
- Uncertainty Quantification: Statistical predictions with inherent confidence intervals
- External Factors: Limited incorporation of global climate indices
- Model Monitoring: Regular performance evaluation recommended
- Data Pipeline: Automated data quality validation protocols
- Version Control: Model versioning and rollback capabilities
- Deep Learning Integration: LSTM networks for sequential pattern recognition
- Ensemble Methods: Multi-model prediction averaging
- Feature Expansion: Integration of additional meteorological variables
- Real-time Data: Live weather API integration
- Regional Models: Province-level prediction capabilities
- Alert Systems: Automated flood risk notifications
- Mobile Interface: Responsive design for field applications
- API Expansion: Enhanced programmatic access features
pakistan_temp_rainfall_predictive_modelling/
├── pakistan_climate_data_analysis_and_predictive_modelling_ahsan_javed.ipynb # Main analysis notebook
├── README.md # Project documentation
├── DATASET_INFO.md # Dataset information and metadata
├── raw_data/ # Historical climate datasets
│ ├── rainfall_1901_2016_pak.csv # Pakistan rainfall data (1901-2016)
│ └── tempreture_1901_2016_pakistan.csv # Pakistan temperature data (1901-2016)
├── images/ # Visualization assets
│ ├── Data_Visualization.png # Exploratory data analysis charts
│ ├── advanced_visualization.png # Advanced statistical visualizations
│ ├── climate_EDA.png # Climate trend analysis
│ ├── flask_app.png # Flask application interface
│ ├── flood_risk_analysis.png # Flood risk assessment charts
│ └── model_evaluation.png # Model performance metrics
├── videos/ # Demo videos
│ └── streamlit_app.mov # Streamlit application demonstration
├── Flask_app/ # Production Flask API service
│ ├── app.py # Main Flask application
│ ├── requirements.txt # Python dependencies
│ ├── rainfall_model_pipeline.joblib # Trained rainfall model
│ ├── temperature_model_pipeline.joblib # Trained temperature model
│ ├── model_metadata.joblib # Model performance metadata
│ ├── smart_inference_function.joblib # Optimized prediction function
│ └── templates/
│ └── index.html # Web interface template
├── Streamlit_app/ # Interactive web dashboard
│ └── streamlit_app.py # Streamlit application
└── flood_risk_analysis_report/ # Analysis reports
└── Pakistan_Weather_Report_20250723_0606.pdf # Comprehensive flood risk report
Main Developer: Ahsan Javed
- Machine Learning Architecture Design
- Feature Engineering Framework Development
- Model Training and Optimization
- Production System Implementation
Data Source: CHISEL @ LUMS (Center for Climate Research and Development) Chisel_website
Analysis Period: 1901-2016 Pakistan Climate Dataset Development Timeline: Comprehensive iterative development with empirical validation
- scikit-learn: Core machine learning framework
- XGBoost/LightGBM: Advanced gradient boosting implementations
- pandas/numpy: Data processing and numerical computation
- matplotlib/seaborn/plotly: Visualization and analysis tools
- Flask/Streamlit: Web application frameworks
For technical inquiries, model improvements, or collaboration opportunities regarding Pakistan's climate prediction capabilities, please contact through the project repository or professional networks.
Disclaimer: This system is designed for research and decision support purposes. For critical flood management decisions, integrate predictions with official meteorological services and consider model limitations within operational contexts.
Research Impact: Contributing to Pakistan's climate resilience through advanced predictive analytics and flood risk assessment capabilities during critical monsoon periods.