malike/ghana-fews-uel

Implementation of a Flood Early Warning System (Not production ready or full sourcecode not available here yet) # GHANA Flood Early Warning System (FEWS) A comprehensive early warning system for flood prediction and alerts in Ghana. ## Overview The GHANA Flood Early Warning System (FEWS) is designed to predict flood risks, issue alerts, and provide critical information to help communities prepare for and respond to flooding events. The system uses machine learning models to analyze weather data and generate flood predictions. ## Architecture The system consists of the following components: - **Backend Service**: Spring Boot Java application that provides the core functionality and APIs. - **ML Service**: Python Flask application that hosts the machine learning models for flood prediction. - **Admin Dashboard**: Web interface for system administrators to manage alerts and monitor the system. - **Public Portal**: Web interface for the general public to access alerts and information. - **Mobile App**: Flutter-based mobile application for end-users to receive alerts and access information. - **Database**: PostgreSQL database with PostGIS extension for storing application data. - **Cache**: Redis for caching frequently accessed data. - **Message Broker**: RabbitMQ for asynchronous communication between services. ## Features - Real-time flood risk prediction based on weather data - Multi-language alert notifications (English, Twi, Hausa, Ewe) - User management with role-based access control - Historical data analysis and visualization - Mobile app for public access to alerts and safety information - Community reporting system for flood observations - Interactive risk maps with geographic visualization - Offline capabilities for areas with limited connectivity - RESTful APIs with OpenAPI documentation ## Prerequisites - Java 11 or higher - Python 3.9 or higher - Docker and Docker Compose - Maven 3.6 or higher - Flutter SDK (for mobile app development) - Node.js 14.x or higher (for frontend development tools) ## Getting Started ### Running with Docker Compose 1. Clone th …