**Introduction
**With the rise in climate-related disasters, the need for intelligent systems that can accurately and rapidly predict natural hazards like wildfires and storms has never been more urgent. Version 190 is a comprehensive project that utilizes an AI Agent to analyze environmental data and predict wildfire occurrences in real time, with automatic updates every five minutes and auto-generated detailed reports to support proactive decision-making.
**Core Idea
**The AI agent in Version 190 processes meteorological and satellite data to forecast wildfires and storms. It uses multiple machine learning models (including Decision Tree, Random Forest, and SVM), in addition to advanced algorithms like XGBoost and LightGBM, to generate high-accuracy predictions for specific geographic locations.
**Key Features
**Real-Time Updates: Environmental data and predictions are updated every 5 minutes.
OpenWeatherMap API Integration: Automatically pulls live weather data for accurate forecasting.
User Interface (GUI): Built with Tkinter, allowing users to select country and city and view prediction percentages (Fire – Storm – No Event).
Multi-Model Prediction: Compares results from various models to improve decision reliability.
Risk Assessment: Evaluates and labels the severity of a fire or storm event if detected.
Automated Excel Reports: Generates structured Excel files with predictions, confidence scores, timestamps, and location details.
Deep Learning Integration: Continuously improves accuracy using deep neural networks.
Visual Analytics: Confusion matrices, AUC-ROC scores, and other metrics are generated for model evaluation.
Visual and Audio Alerts: The GUI includes real-time alert features when a critical event is predicted.
Drone Integration (Planned): The system is designed for future integration with drones that detect, report, and extinguish fires with onboard suppression systems and real-time imagery.
**How the AI Agent Works
**Inputs: Weather data (temperature, humidity, wind speed), satellite data (brightness, acquisition time, fire radiative power).
Processing: Cleaned and analyzed using a mix of ML and DL models within a time-scheduled pipeline.
Outputs: Fire or storm probability (%), risk level, and Excel reports for each update cycle.
GUI: Allows location input and presents predictions in a human-friendly format.
**Social and Environmental Impact
**Version 190 helps protect lives, ecosystems, and property through early detection and forecasting of environmental disasters. It serves as a powerful example of how AI can play a crucial role in environmental protection, especially when integrated with local authorities or emergency response systems.
**Challenge Relevance
**This project was built to function as a self-reliant AI agent capable of thinking, analyzing, and acting without human intervention when it comes to environmental threats. For this reason, it is submitted to the Runner H – AI Agent Prompting Challenge, demonstrating how an agent can interact with real-world data and generate real-time intelligent decisions.
Conclusion
Version 190 is more than just a prediction model. It’s a complete AI-powered system designed to analyze, forecast, and respond to environmental hazards independently. With further development, it could serve as a global standard for AI-driven disaster prediction and management.
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