EcoGuard AI

Research Problem

Environmental risks are increasing in frequency and complexity, making them difficult to predict and manage. This is mainly due to fragmented monitoring systems, delayed data reporting, and the lack of real-time insights. As a result, decision-makers and communities often lack timely and actionable information to respond effectively to environmental threats.

Objectives

• Collect continuous real-time environmental data using IoT sensors and external sources
• Detect anomalies and predict environmental risks using AI and machine learning models
• Generate early warnings and alerts for critical situations
• Provide an intuitive and user-friendly dashboard for effective decision-making

Methodology

EcoGuard-AI follows a structured pipeline that includes real-time data acquisition, preprocessing, feature engineering, model training, and visualization. The system integrates IoT sensors, backend processing, machine learning models, and a web-based dashboard to deliver accurate insights and timely alerts for environmental risk management.