Project Resources
These are the libraries, data sources, and tools that power our APAW project.
Core Algorithms & Libraries
Machine learning algorithm used for classification (Flood/No Flood) and regression (Flood Depth) prediction.
Long Short-Term Memory neural networks explored for time-series classification and regression of flood events.
Core Python library providing machine learning algorithms, preprocessing tools, and evaluation metrics.
Deep learning framework used to build and train the LSTM neural network models.
Essential data manipulation and analysis library used extensively for handling time-series and tabular data.
Library extending Pandas for geospatial data processing, used in offline preparation of map-based features.
Python package for manipulation and analysis of planar geometric objects (used via GeoPandas).
Python package used for retrieving and analyzing OpenStreetMap data during offline feature preparation.
Python library used for efficiently saving and loading trained Scikit-learn models and pipelines.
Standard Python library for making HTTP requests to fetch data from external APIs.
Data Sources
Philippine Atmospheric, Geophysical and Astronomical Services Administration - provided real-time and historical water level station data.
Provided historical and forecast weather parameters including precipitation, temperature, humidity, wind, cloud cover, and soil conditions.
Provided supplementary weather forecast data used in the prediction backend.
Utilized for sourcing baseline geographic features (like waterways for offline analysis) and location context via Nominatim.
Historical Flood Event Records
Compiled from publicly available reports and advisories issued by relevant government agencies during past weather events.
Development & Deployment Stack
Primary programming language used for backend API, data processing, and model development.
Cloud-based development environment used for initial data processing, model training, and evaluation.
Framework used to rapidly build and deploy the web interface for the backend prediction API.
Backend-as-a-Service platform used for storing and retrieving pre-fetched weather/soil forecast data.
JavaScript library used for creating interactive maps within the frontend application.
Geocoding service based on OpenStreetMap data used for location search functionality.
Geocoding API based on OpenStreetMap data used to find locations and return their coordinates for the search functionality.
Other Tools
Web application used during data collection phase for organizing and finding location coordinates.
GitHub repository providing administrative boundary data (GeoJSON) used for NCR context/visualization during development.
Institution
College of Computer Studies - Academic institution supporting this capstone project.