About APAW
Applying supervised learning models to analyze integrated hydrometeorological data and predict potential flood occurrences within Metro Manila.
What is APAW?
APAW (Advanced Predictive Analysis of Water-related Flood Risk) is an innovative flood prediction platform developed as part of our academic capstone project. Using advanced machine learning techniques, APAW analyzes weather patterns, geographical features, and historical data to predict flood risks with hourly prediction of up to 5 days in advance.
Our system addresses a critical gap in flood preparedness by providing location-specific predictions for any area within Metro Manila, moving beyond traditional regional flood warnings to enable proactive community preparation.
Technical Achievements
Our system demonstrates strong performance across multiple metrics
High Accuracy Classification
Our flood detection model achieves 88% accuracy in determining whether flooding will occur, with a 73% recall rate for identifying potential flood events.
Reliable Depth Prediction
When floods are predicted, our LSTM model estimates flood depth with a Mean Absolute Error of just 8.07 cm, providing highly valuable information for preparation.
Hourly Forecast Window
Unlike traditional same-day flood warnings, APAW provides hourly predictions up to 5 days in advance, enabling better preparation time.
Fast Response Time
Our optimized system delivers predictions in 5-15 seconds, making it practical for real-time decision making.
Our Methodology
A sophisticated four-step process combining traditional ML and deep learning
Data Integration
We combine weather forecasts, historical flood records, geographic features, and soil conditions from multiple reliable sources to create a comprehensive dataset.
Flood Detection with Random Forest
Our Random Forest classifier analyzes patterns in the integrated data to predict whether flooding will occur at a specific location and time.
Depth Prediction with LSTM
When flooding is detected, our LSTM neural network processes temporal sequences to accurately predict flood depth by learning from historical water level patterns.
Real-time Forecasting
The system continuously processes current weather conditions to generate updated hourly flood risk forecasts up to 5 days ahead for any location in Metro Manila.
APAW vs Traditional Systems
See how our platform advances beyond conventional flood warning systems
| Feature | APAW Platform | Traditional Systems |
|---|---|---|
| Prediction Timeframe | Hourly forecasts up to 5 days in advance | Same-day or short-term warnings (hours ahead) |
| Coverage Area | Any specific location in Metro Manila | Regional coverage (e.g., NCR flood risk) |
| Information Provided | Specific flood depth predictions | General flood alerts |
| Accessibility | Public web platform | Government announcements and media |
Our Mission & Vision
Our Mission
Our mission is to enhance community resilience and safety by developing and providing accessible, data-driven predictions for water-related flood risks, empowering proactive preparedness and response.
Our Vision
Our vision is a future where communities are proactively safeguarded against flood impacts, utilizing accurate and timely predictive insights to minimize risk to lives, property, and livelihoods.
Our Core Values
The principles that guide our work, embodied in our name: APAW
Accuracy
We are committed to thorough analysis and continuous improvement to provide the most reliable flood risk predictions possible, ensuring our models deliver precise and dependable results.
Preparedness
We empower individuals, communities, and authorities with timely information that enables proactive planning and effective preparation for potential flood events before they occur.
Accessibility
We strive to make complex flood risk information understandable and readily available to everyone through user-friendly interfaces and clear, actionable communication.
Wisdom
We leverage data-driven insights and cutting-edge machine learning to transform raw information into actionable knowledge that helps communities make informed decisions about flood risks.