{"id":28924347,"url":"https://github.com/bevinaa/forest-fire-detection-system","last_synced_at":"2026-02-22T09:42:46.281Z","repository":{"id":303234023,"uuid":"1014811743","full_name":"Bevinaa/Forest-Fire-Detection-System","owner":"Bevinaa","description":"An IoT-enabled Forest Fire Detection System that uses NodeMCU, environmental sensors, Flutter mobile app, and an XGBoost ML model to monitor and predict fire risks in real-time.","archived":false,"fork":false,"pushed_at":"2025-07-27T11:43:48.000Z","size":5585,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-29T09:54:37.660Z","etag":null,"topics":["dht11-sensor","flask-api","flutter","iot","machine-learning","mq2-sensor","mysql-database","nodemcu","python"],"latest_commit_sha":null,"homepage":"","language":"Dart","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Bevinaa.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-07-06T13:16:24.000Z","updated_at":"2025-07-27T11:43:52.000Z","dependencies_parsed_at":"2025-07-09T20:02:58.168Z","dependency_job_id":null,"html_url":"https://github.com/Bevinaa/Forest-Fire-Detection-System","commit_stats":null,"previous_names":["bevinaa/forestfiredetectionsystem","bevinaa/forest-fire-detection-system"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Bevinaa/Forest-Fire-Detection-System","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bevinaa%2FForest-Fire-Detection-System","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bevinaa%2FForest-Fire-Detection-System/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bevinaa%2FForest-Fire-Detection-System/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bevinaa%2FForest-Fire-Detection-System/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Bevinaa","download_url":"https://codeload.github.com/Bevinaa/Forest-Fire-Detection-System/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bevinaa%2FForest-Fire-Detection-System/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29708363,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-22T05:59:28.568Z","status":"ssl_error","status_checked_at":"2026-02-22T05:58:46.208Z","response_time":110,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dht11-sensor","flask-api","flutter","iot","machine-learning","mq2-sensor","mysql-database","nodemcu","python"],"created_at":"2025-06-22T10:39:52.703Z","updated_at":"2026-02-22T09:42:46.265Z","avatar_url":"https://github.com/Bevinaa.png","language":"Dart","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **Forest Fire Detection System**\n\n![Platform](https://img.shields.io/badge/platform-IoT%20%7C%20Flutter%20%7C%20Python%20%7C%20ML-green)\n![Status](https://img.shields.io/badge/status-Project%20Complete-brightgreen)\n\n## **Overview**\n\nThis repository contains an **IoT-enabled Forest Fire Detection System** designed to monitor environmental conditions and predict fire risk in real time. It includes a **Flutter-based mobile application**, sensor data fetching using **NodeMCU**, and a basic **machine learning model - xgboost** for fire prediction.\n\nThe system monitors temperature, humidity, and smoke levels, and visualizes the data in a user-friendly mobile interface.\n\n---\n\n## **Key Features**\n\n- **Real-Time Monitoring**: Tracks temperature, humidity, and smoke levels from sensors.\n- **Mobile Dashboard**: Flutter app displays live data and predictions in a clean UI.\n- **ML-Based Prediction**: XGBoost model trained to classify whether sensor readings indicate fire.\n- **IoT Integration**: NodeMCU fetches and transmits data using Wi-Fi.\n- **Database Storage**: Sensor values and predictions are stored in a MySQL database.\n- **Resident Alert System**: Button in the app to notify nearby residents with safety instructions.\n- **Expandable**: Future integration with AR/VR fire spread simulations and remote server dashboards.\n\n---\n\n## **Technologies Used**\n\n- **Flutter**: For the mobile UI.\n- **NodeMCU**: For data collection from DHT11, MQ-2 sensors, etc.\n- **Python \u0026 jupyter**: For training and running the ML model.\n- **MySQL**: For storing and syncing sensor data.\n- **C and Arduino IDE**: For programming microcontrollers.\n\n---\n\n## Tech Stack\n\n| Technology         | Purpose                                      |\n|--------------------|----------------------------------------------|\n| **Flutter**        | Mobile app for real-time monitoring          |\n| **NodeMCU**        | IoT microcontroller for sensor interfacing   |\n| **MQ-2 Sensor**    | Smoke detection                              |\n| **DHT11 Sensor**   | Temperature and humidity measurement         |\n| **Python**         | Machine learning model development           |\n| **XGBoost**        | ML model for fire risk classification        |\n| **MySQL**          | Backend database                             |\n| **C (Arduino IDE)**| Microcontroller programming                  |\n\n---\n\n## **Pre-requisites**\n\nTo run this project, ensure you have the following installed:\n\n- **Flutter SDK** (latest version)\n- **Python 3.7+**\n- **Arduino IDE** (for NodeMCU)\n- **Hardware**: DHT11, MQ-2, NodeMCU, Breadboard, Jumper wires\n- **Database**: MySQL account for backend data storage\n\n---\n\n## How It Works\n\n1. **Sensor Layer**  \n   The DHT11 and MQ-2 sensors measure environmental conditions and transmit data via NodeMCU.\n\n2. **Data Transmission**  \n   NodeMCU sends sensor readings over Wi-Fi to a backend server (or directly to the mobile app via Firebase/MySQL).\n\n3. **Prediction Engine**  \n   The ML model (XGBoost) predicts fire risk using the incoming data.\n\n4. **User Interface**  \n   A Flutter-based mobile app displays:\n   - Live data (temperature, humidity, smoke)\n   - Fire prediction result\n   - Alert functionality for residents\n\n---\n\n## Machine Learning Details\n\n- **Algorithm**: XGBoost Classifier  \n- **Input Features**: Temperature, Humidity, Smoke Levels  \n- **Training Dataset**: Custom collected sensor readings under fire and non-fire conditions  \n- **Accuracy**: ~96.4%  \n- **Evaluation Metrics**: Precision, Recall, F1-score, Confusion Matrix  \n\n\u003e  The model is lightweight and optimized for fast predictions on limited hardware setups.\n\n---\n\n| Installation and Setup |\n|-----------------------------------|\n| ![image](https://github.com/user-attachments/assets/3512f0df-1c7e-41f3-9852-35464a893a21) |\n\n---\n\n| Demo of the Project |\n|-----------------------------------|\n| https://github.com/Bevinaa/Forest-Fire-Detection-System/demo.mp4 |\n\n---\n\n##  Screenshots\n\n| Mobile Application | Fire Prediction |\n|------------------|-----------------|\n| ![image](https://github.com/user-attachments/assets/79f30814-2d28-41da-ba58-adaac74f764b) | ![image](https://github.com/user-attachments/assets/87abae7f-878e-41f0-8307-ef5bd92bb24f) |\n\n---\n\n| Analytics Dashboard |\n|-----------------------------------|\n| ![image](https://github.com/user-attachments/assets/c23c55fe-4d2a-4251-a24c-3d87d85d7a6c) |\n|![image](https://github.com/user-attachments/assets/17752d7c-3bd6-483e-bfc8-627900d74fee) |\n\n---\n## Contact\n\nAuthor: **Bevina R**  \nEmail: bevina2110@gmail.com  \nGitHub: [Bevinaa](https://github.com/Bevinaa)\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbevinaa%2Fforest-fire-detection-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbevinaa%2Fforest-fire-detection-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbevinaa%2Fforest-fire-detection-system/lists"}