IConfy/Rwanda-Climate-Resilience

Developed for the NISR 2025 Hackathon (Track 3: Climate Resilience & Disaster Risk Management), this project leverages real-time climate data from Open-Meteo and geolocation technology to assess weather-related risks and alert communities in vulnerable areas. # Rwanda Climate Resilience Dashboard A static frontend application designed to monitor climate-related data in Rwanda and provide alerts based on anomaly detection. This project is intended for deployment on static hosting platforms like GitHub Pages. ## Overview This dashboard provides a visualization of climate data for various districts in Rwanda. It fetches real-time weather information from the Open-Meteo API, compares it against baseline data, and flags anomalies. The frontend is built with vanilla HTML, CSS, and JavaScript. ## Features * **Real-time Data:** Fetches current weather data from Open-Meteo. * **Anomaly Detection:** Simple detection mechanism to identify significant deviations from baseline climate data. * **Data Visualization:** (Future goal) Map-based visualization of districts and climate alerts. * **Supabase Integration:** Uses Supabase for data storage and retrieval. ## Tech Stack * **Frontend:** HTML, CSS, JavaScript * **Data Source:** Open-Meteo API * **Database:** Supabase ## Getting Started To get a local copy up and running, follow these simple steps. ### Prerequisites * A modern web browser. * A Supabase account. ### Installation 1. **Clone the repo** ```sh git clone github.com ``` 2. **Configure Supabase** Open `assets/config.js` and add your Supabase Project URL and Anon Key: ```javascript const SUPABASE_URL = 'YOUR_SUPABASE_URL'; const SUPABASE_ANON_KEY = 'YOUR_SUPABASE_ANON_KEY'; ``` ## Database Setup The project relies on a Supabase backend for data persistence. ### Schema The following tables are required: * `district_baselines`: Stores baseline climate data for each district. * `id` (pk) * `name` * `latitude` * `longitude` * `avg_rainfall` * `avg_temperature` * `district_updates`: Logs real-time weather data. * `id` (pk) * `district_id` (fk to `district_baselines`) * `timestamp` * `rainfall` * `temperature` * `humidity` …